A heart stroke, also known as a myocardial infarction or heart attack, is a critical medical condition that arises when there is an obstruction in the coronary arteries that provide blood to the heart muscles. This blockage results in a diminished flow of blood and oxygen to a specific area of the heart. This abrupt interruption initiates a gradual sequence of heart muscle damage, which can lead to varying degrees of functional impairment. The severity of these impairments is primarily determined by the precise location of the heart muscle affected. Therefore, it is of utmost importance to identify the warning signs and symptoms of a stroke as soon as possible. This is the objective of this paper is to early recognition and prompt action can significantly improve the chances of a healthy and fulfilling life following a stroke. In this research work, the Stroke dataset is pre-processed and on pre-processed dataset machine learning and ensemble machine learning techniques were employed to develop and assess several models aimed at creating a stable framework for predicting the enduring stroke risk. And various matrices like accuracy, F1 score, ROC, precision, and recall are calculated. Among all models, AdaBoost model demonstrated exceptional performance validated through multiple metrics, including Precision, AUC, recall, accuracy, and F1-measure. The results underscored superiority of the AdaBoost classification method, achieving an impressive Accuracy of 99%. AdaBoost model may serve as a stable framework for predicting enduring stroke risk, emphasizing its potential utility in clinical settings for identifying individuals at higher risk of experiencing a stroke.
心脏中风又称心肌梗塞或心脏病发作，是一种严重的医疗状况，当为心脏肌肉提供血液的冠状动脉发生阻塞时就会发生。这种阻塞导致流向心脏特定区域的血液和氧气减少。这种突然的中断会导致心肌逐渐受损，从而导致不同程度的功能障碍。这些损伤的严重程度主要取决于心肌受影响的确切位置。因此，尽快识别中风的预警信号和症状至关重要。本文的目的就是要及早识别并迅速采取行动，从而大大提高中风患者健康、充实生活的机会。在这项研究工作中，对中风数据集进行了预处理，并在预处理数据集上采用了机器学习和集合机器学习技术来开发和评估多个模型，旨在创建一个稳定的框架来预测持久的中风风险。并计算了各种矩阵，如准确率、F1 分数、ROC、精确度和召回率。在所有模型中，AdaBoost 模型通过多个指标（包括精确度、AUC、召回率、准确度和 F1 测量）验证，表现出卓越的性能。结果凸显了 AdaBoost 分类方法的优越性，准确率达到了令人印象深刻的 99%。AdaBoost 模型可作为预测持久中风风险的稳定框架，强调了其在临床环境中识别中风高危人群的潜在用途。
{"title":"Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification","authors":"Purnima Pal, Manju Nandal, Srishti Dikshit, Aarushi Thusu, Harsh Vikram Singh","doi":"10.4108/eetpht.9.4617","DOIUrl":"https://doi.org/10.4108/eetpht.9.4617","url":null,"abstract":"A heart stroke, also known as a myocardial infarction or heart attack, is a critical medical condition that arises when there is an obstruction in the coronary arteries that provide blood to the heart muscles. This blockage results in a diminished flow of blood and oxygen to a specific area of the heart. This abrupt interruption initiates a gradual sequence of heart muscle damage, which can lead to varying degrees of functional impairment. The severity of these impairments is primarily determined by the precise location of the heart muscle affected. Therefore, it is of utmost importance to identify the warning signs and symptoms of a stroke as soon as possible. This is the objective of this paper is to early recognition and prompt action can significantly improve the chances of a healthy and fulfilling life following a stroke. In this research work, the Stroke dataset is pre-processed and on pre-processed dataset machine learning and ensemble machine learning techniques were employed to develop and assess several models aimed at creating a stable framework for predicting the enduring stroke risk. And various matrices like accuracy, F1 score, ROC, precision, and recall are calculated. Among all models, AdaBoost model demonstrated exceptional performance validated through multiple metrics, including Precision, AUC, recall, accuracy, and F1-measure. The results underscored superiority of the AdaBoost classification method, achieving an impressive Accuracy of 99%. AdaBoost model may serve as a stable framework for predicting enduring stroke risk, emphasizing its potential utility in clinical settings for identifying individuals at higher risk of experiencing a stroke.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"133 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a model to address the challenges faced by medical cyber-physical systems (MCPS) by implementing a permissioned blockchain platform. The platform incorporates the unique properties of blockchain into the network of affected systems, including decentralization, transparency, and immutability. The platform also includes a novel technique to secure MCPS through an automated access-control manager. This manager allows users to control who has access to their data, and can be configured to trust a third party if desired. The paper also extends into networked medical device systems, and discusses how the platform can be used to address critical is-sues specific to this field, such as network design. Finally, the paper discusses how various security features can be integrated into ultra-small devices, enhancing the protection of embedded systems. The overall objective of this research is to develop a secure and efficient data transfer mechanism for MCPS. The proposed platform addresses the challenges faced by MCPS by incorporating the unique properties of blockchain.
{"title":"Blockchain-Enabled Hyperledger Fabric to Secure Data Transfer Mechanism for Medical Cyber-Physical System: Overview, Issues, and Challenges","authors":"P. Vinayasree, A. Mallikarjuna Reddy","doi":"10.4108/eetpht.9.4518","DOIUrl":"https://doi.org/10.4108/eetpht.9.4518","url":null,"abstract":"This paper proposes a model to address the challenges faced by medical cyber-physical systems (MCPS) by implementing a permissioned blockchain platform. The platform incorporates the unique properties of blockchain into the network of affected systems, including decentralization, transparency, and immutability. The platform also includes a novel technique to secure MCPS through an automated access-control manager. This manager allows users to control who has access to their data, and can be configured to trust a third party if desired. The paper also extends into networked medical device systems, and discusses how the platform can be used to address critical is-sues specific to this field, such as network design. Finally, the paper discusses how various security features can be integrated into ultra-small devices, enhancing the protection of embedded systems. The overall objective of this research is to develop a secure and efficient data transfer mechanism for MCPS. The proposed platform addresses the challenges faced by MCPS by incorporating the unique properties of blockchain.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139203796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saifeddine Ben Nasr, Imen Messaoudi, Afef Elloumi Oueslati, Z. Lachiri
INTRODUCTION: Breast cancer (BC) is the most commonly occurring cancer and the second leading cause for women’s disease death. The BC cases are associated with genital mutations which are inherited from older generations or acquired overtime. If the diagnosis is done at the first stage, effects associated with certain treatments can be limited, costs can be saved and the diagnostic time can be minimized. This can also help specialists target the best treatment to increase the rate of cures. Nevertheless, its discovery in patients is very challenging due to silent symptoms aside from the fact the routine screening is not recommended for women under 40 years old.OBJECTIVES: Several efforts are aimed at the BC early detection using machine and deep learning systems. The proposed algorithms use different data types to distinguish between cancerous and non-cancerous cases; as: mammography, ultrasound and MRI (magnetic resonance imaging) images. Then, different learning tools were applied on this data for the classification task. Despite the classification rates which exceed 90%, the major drawback of all these methods is that they are applicable only after the appearance of the cancerous tumors, which reduces the cure rates.METHODS: We propose a new technique for early breast cancer screening. For the data, we focus on cancerous and non-cancerous SNP (Single Nucleotide Polymorphism) protein sequences of the TP53 gene in chromosome 17. This gene is shown to be linked to different single amino acid mutations on which we will shed light here. The method we propose transforms SNP textual sequences into digital vectors via coding. Then, RGB scalogram images are generated using the continuous wavelet transform. A pretreatment of color coefficients is applied to scalograms aiming at creating four different databases. Finally, a CNN deep learning network is used for the binary classification of cancerous and non-cancerous images.RESULTS: During the validation process, we reached good performance with specificity of 97.84%, sensitivity of 96.45%, an overall accuracy of 95.29% and an equal run time of 12 minutes 3 seconds. These values ensure the efficiency of our method.To enhance more these results, we used the ORB feature detection technique. Consequently, the classification rates have been improved to reach 95.9% as accuracyCONCLUSION: Our method will allow significant savings time and lives by detecting the disease in patients whose genetic mutations are beginning to appear.
{"title":"Breast cancer early detection in TP53 SNP protein sequences based on a new Convolutional Neural Network model","authors":"Saifeddine Ben Nasr, Imen Messaoudi, Afef Elloumi Oueslati, Z. Lachiri","doi":"10.4108/eetpht.9.3218","DOIUrl":"https://doi.org/10.4108/eetpht.9.3218","url":null,"abstract":"INTRODUCTION: Breast cancer (BC) is the most commonly occurring cancer and the second leading cause for women’s disease death. The BC cases are associated with genital mutations which are inherited from older generations or acquired overtime. If the diagnosis is done at the first stage, effects associated with certain treatments can be limited, costs can be saved and the diagnostic time can be minimized. This can also help specialists target the best treatment to increase the rate of cures. Nevertheless, its discovery in patients is very challenging due to silent symptoms aside from the fact the routine screening is not recommended for women under 40 years old.OBJECTIVES: Several efforts are aimed at the BC early detection using machine and deep learning systems. The proposed algorithms use different data types to distinguish between cancerous and non-cancerous cases; as: mammography, ultrasound and MRI (magnetic resonance imaging) images. Then, different learning tools were applied on this data for the classification task. Despite the classification rates which exceed 90%, the major drawback of all these methods is that they are applicable only after the appearance of the cancerous tumors, which reduces the cure rates.METHODS: We propose a new technique for early breast cancer screening. For the data, we focus on cancerous and non-cancerous SNP (Single Nucleotide Polymorphism) protein sequences of the TP53 gene in chromosome 17. This gene is shown to be linked to different single amino acid mutations on which we will shed light here. The method we propose transforms SNP textual sequences into digital vectors via coding. Then, RGB scalogram images are generated using the continuous wavelet transform. A pretreatment of color coefficients is applied to scalograms aiming at creating four different databases. Finally, a CNN deep learning network is used for the binary classification of cancerous and non-cancerous images.RESULTS: During the validation process, we reached good performance with specificity of 97.84%, sensitivity of 96.45%, an overall accuracy of 95.29% and an equal run time of 12 minutes 3 seconds. These values ensure the efficiency of our method.To enhance more these results, we used the ORB feature detection technique. Consequently, the classification rates have been improved to reach 95.9% as accuracyCONCLUSION: Our method will allow significant savings time and lives by detecting the disease in patients whose genetic mutations are beginning to appear.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139220989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Albe Bing Zhe Chai, Bee Theng Lau, Mark Kit Tsun Tee, Christopher McCarthy
INTRODUCTION: Road Traffic Accidents (RTAs) patterns discovery is vital to formulate mitigation strategies based on the characteristics of RTAs.OBJECTIVES: Various studies have utilised Apriori algorithm for RTA pattern discovery. Hence, this work aimed to explore the applicability of FP-Growth algorithm to discover and compare the RTA patterns in several regions.METHODS: Orange data mining toolkit is used to discover RTA patterns from the open-access RTA datasets from Addis Ababa city (12,317 samples), Finland (371,213 samples), Berlin city-state (50,119 samples), New Zealand (776,878 samples), the UK (1,048,575 samples), and the US (173,829 samples).RESULTS: There are similarities and differences in RTA patterns among the six regions. The five common factors contributing to RTAs are road characteristics, type of road users or objects involved, environment, driver’s profile, and characteristics of RTA location. These findings could be beneficial for the authorities to formulate strategies to reduce the risk of RTAs.CONCLUSION: Discovery of RTA patterns in different regions is beneficial and future work is essential to discover the RTA patterns from different perspectives such as seasonal or periodical variations of RTA patterns.
{"title":"Comparative analysis of regional variations in road traffic accident patterns with association rule mining","authors":"Albe Bing Zhe Chai, Bee Theng Lau, Mark Kit Tsun Tee, Christopher McCarthy","doi":"10.4108/eetpht.9.3173","DOIUrl":"https://doi.org/10.4108/eetpht.9.3173","url":null,"abstract":"INTRODUCTION: Road Traffic Accidents (RTAs) patterns discovery is vital to formulate mitigation strategies based on the characteristics of RTAs.OBJECTIVES: Various studies have utilised Apriori algorithm for RTA pattern discovery. Hence, this work aimed to explore the applicability of FP-Growth algorithm to discover and compare the RTA patterns in several regions.METHODS: Orange data mining toolkit is used to discover RTA patterns from the open-access RTA datasets from Addis Ababa city (12,317 samples), Finland (371,213 samples), Berlin city-state (50,119 samples), New Zealand (776,878 samples), the UK (1,048,575 samples), and the US (173,829 samples).RESULTS: There are similarities and differences in RTA patterns among the six regions. The five common factors contributing to RTAs are road characteristics, type of road users or objects involved, environment, driver’s profile, and characteristics of RTA location. These findings could be beneficial for the authorities to formulate strategies to reduce the risk of RTAs.CONCLUSION: Discovery of RTA patterns in different regions is beneficial and future work is essential to discover the RTA patterns from different perspectives such as seasonal or periodical variations of RTA patterns.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139226089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Rapid developments in deep learning (DL) techniques have made it possible to find and recognize objects in pictures. To create a network that is significantly more successful than a single CNN, GAN, RNN, etc., we can mix various neural network models (CNN, GAN, RNN).this combination is known as hybrid model. Hybrid model of deep leaning is give more accurately result for detection and identification of paddy diseases. OBJECTIVES: I have studies outcome of hybrid model 1(DCNN+SVM) and Hybrid model 2 (DCNN + Transfer Learning) to increase accuracy of Rice plant disease detection and classification. The Researched model detects multiple rice plant diseases and it is giving same result in multiple data sets. METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The brand new model is DST where D stands for DCNN, S stands for SVM and T stands for transfer learning. RESULTS: The Researched DST model achieved 95% Training accuracy and 85% validation Accuracy. The Researched model detect multiple rice plant diseases and it is giving same result in multiple data set. CONCLUSION: The proposed model combined 2 existing model and developed hybrid model that a detect various rice plant diseases with better accuracy from available existing model.
{"title":"Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning","authors":"A. Dixit, Rajat Verma","doi":"10.4108/eetpht.9.4481","DOIUrl":"https://doi.org/10.4108/eetpht.9.4481","url":null,"abstract":"INTRODUCTION: Rapid developments in deep learning (DL) techniques have made it possible to find and recognize objects in pictures. To create a network that is significantly more successful than a single CNN, GAN, RNN, etc., we can mix various neural network models (CNN, GAN, RNN).this combination is known as hybrid model. Hybrid model of deep leaning is give more accurately result for detection and identification of paddy diseases. OBJECTIVES: I have studies outcome of hybrid model 1(DCNN+SVM) and Hybrid model 2 (DCNN + Transfer Learning) to increase accuracy of Rice plant disease detection and classification. The Researched model detects multiple rice plant diseases and it is giving same result in multiple data sets. METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The brand new model is DST where D stands for DCNN, S stands for SVM and T stands for transfer learning. RESULTS: The Researched DST model achieved 95% Training accuracy and 85% validation Accuracy. The Researched model detect multiple rice plant diseases and it is giving same result in multiple data set. CONCLUSION: The proposed model combined 2 existing model and developed hybrid model that a detect various rice plant diseases with better accuracy from available existing model.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Activity-based wellness management is thought to be a powerful application for mobile health. It is possible to provide context-aware wellness services and track human activity thanks to accessing for multiple devices as well as gadgets that we use every day. Generally in smart gadgets like phones, watches, rings etc., the embedded sensors having a wealth data that can be incorporated to person task tracking identification. In a real-world setting, all researchers shown effective boosting algorithms can extract information in person task identification. Identifying basic person tasks such as talk, walk, sit along sleep. Our findings demonstrate that boosting classifiers perform better than conventional machine learning classifiers. Moreover, the feature engineering for differentiating an activity detection capability for smart phones and smart watches. For the purpose of improving the classification of fundamental human activities, upcoming mechanisms give the guidelines for identification for various sensors and wearable devices.
{"title":"A Deep Survey on Human Activity Recognition Using Mobile and Wearable Sensors","authors":"Shaik Jameer, Hussain Syed","doi":"10.4108/eetpht.9.4483","DOIUrl":"https://doi.org/10.4108/eetpht.9.4483","url":null,"abstract":"Activity-based wellness management is thought to be a powerful application for mobile health. It is possible to provide context-aware wellness services and track human activity thanks to accessing for multiple devices as well as gadgets that we use every day. Generally in smart gadgets like phones, watches, rings etc., the embedded sensors having a wealth data that can be incorporated to person task tracking identification. In a real-world setting, all researchers shown effective boosting algorithms can extract information in person task identification. Identifying basic person tasks such as talk, walk, sit along sleep. Our findings demonstrate that boosting classifiers perform better than conventional machine learning classifiers. Moreover, the feature engineering for differentiating an activity detection capability for smart phones and smart watches. For the purpose of improving the classification of fundamental human activities, upcoming mechanisms give the guidelines for identification for various sensors and wearable devices.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Edmundo Cabezas-Heredia, Fernando Molina-Granja, Gregory Montenegro-Bosquez, Mónica Salazar, J. Santillán-Lima, Santiago Ramirez, Orestes Cachay-Boza
INTRODUCTION: Stress, a natural reaction of the body to challenging circumstances, can manifest itself in different ways and harm both an individual's physical and mental health. From a constant feeling of being overwhelmed to difficulties in concentration and decision-making, stress can undermine the overall quality of life. Physical symptoms such as headaches, digestive disorders and trouble falling asleep often accompany this condition, highlighting its negative impact on the body. OBJECTIVES: The research aims to determine stress levels in teachers, workers, and university students. METHODS: The stress test proposed by Dr. Gloria Villalobos was applied and complemented with sociodemographic variables. The population consisted of 224 teachers, 11 staff and 32 students. RESULTS: The result found to be stress: 4.5% medium, 27.7% high, and 67.8% very high; The correlation is established employing Cramer's V between the variables and the applied test that the results do not influence the phenomenon investigated CONCLUSION: It concludes the significant presence of medium - high - very high stress in the sample analyzed with serious consequences for health being necessary emerging measures to prevent diseases in university staff.
{"title":"Assessment of technological stress levels in university staff: case study","authors":"Edmundo Cabezas-Heredia, Fernando Molina-Granja, Gregory Montenegro-Bosquez, Mónica Salazar, J. Santillán-Lima, Santiago Ramirez, Orestes Cachay-Boza","doi":"10.4108/eetpht.9.4471","DOIUrl":"https://doi.org/10.4108/eetpht.9.4471","url":null,"abstract":"INTRODUCTION: Stress, a natural reaction of the body to challenging circumstances, can manifest itself in different ways and harm both an individual's physical and mental health. From a constant feeling of being overwhelmed to difficulties in concentration and decision-making, stress can undermine the overall quality of life. Physical symptoms such as headaches, digestive disorders and trouble falling asleep often accompany this condition, highlighting its negative impact on the body. OBJECTIVES: The research aims to determine stress levels in teachers, workers, and university students. METHODS: The stress test proposed by Dr. Gloria Villalobos was applied and complemented with sociodemographic variables. The population consisted of 224 teachers, 11 staff and 32 students. RESULTS: The result found to be stress: 4.5% medium, 27.7% high, and 67.8% very high; The correlation is established employing Cramer's V between the variables and the applied test that the results do not influence the phenomenon investigated CONCLUSION: It concludes the significant presence of medium - high - very high stress in the sample analyzed with serious consequences for health being necessary emerging measures to prevent diseases in university staff.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"208 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139240996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Analysis of magnetic resonance angiography image data is crucial for early detection and prevention of stroke patients. Extracting the 3D Skeleton of cerebral vessels is the focus and difficulty of analysis. OBJECTIVES: The objective is to remove other tissue components from the vascular tissue portion of the image with minimal loss by reading MRA image data and performing processing processes such as grayscale normalization, interpolation, breakpoint detection and repair, and image segmentation to facilitate 3D reconstruction of cerebral blood vessels and the reconstructed vascular tissues make extraction of the Skeleton easier. METHODS: Considering that most of the existing techniques for extracting the 3D vascular Skeleton are corrosion algorithms, machine learning algorithms require high hardware resources, a large number of learning and test cases, and the accuracy needs to be confirmed, an average plane center of mass computation method is proposed, which improves the average plane algorithm by combining the standard plane algorithm and the center of mass algorithm. RESULTS: Intersection points and skeleton breakpoints on the Skeleton are selected as critical points and manually labeled for experimental verification, and the algorithm has higher efficiency and accuracy than other algorithms in directly extracting the 3D Skeleton of blood vessels. CONCLUSION: The method has low hardware requirements, accurate and reliable image data, can be automatically modeled and calculated by Python program, and meets the needs of clinical applications under information technology conditions.
{"title":"Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm","authors":"Guoying Feng, Jie Zhu, Jun Li","doi":"10.4108/eetpht.9.4450","DOIUrl":"https://doi.org/10.4108/eetpht.9.4450","url":null,"abstract":"INTRODUCTION: Analysis of magnetic resonance angiography image data is crucial for early detection and prevention of stroke patients. Extracting the 3D Skeleton of cerebral vessels is the focus and difficulty of analysis. OBJECTIVES: The objective is to remove other tissue components from the vascular tissue portion of the image with minimal loss by reading MRA image data and performing processing processes such as grayscale normalization, interpolation, breakpoint detection and repair, and image segmentation to facilitate 3D reconstruction of cerebral blood vessels and the reconstructed vascular tissues make extraction of the Skeleton easier. METHODS: Considering that most of the existing techniques for extracting the 3D vascular Skeleton are corrosion algorithms, machine learning algorithms require high hardware resources, a large number of learning and test cases, and the accuracy needs to be confirmed, an average plane center of mass computation method is proposed, which improves the average plane algorithm by combining the standard plane algorithm and the center of mass algorithm. RESULTS: Intersection points and skeleton breakpoints on the Skeleton are selected as critical points and manually labeled for experimental verification, and the algorithm has higher efficiency and accuracy than other algorithms in directly extracting the 3D Skeleton of blood vessels. CONCLUSION: The method has low hardware requirements, accurate and reliable image data, can be automatically modeled and calculated by Python program, and meets the needs of clinical applications under information technology conditions.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"721 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images.
OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored.
METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures.
RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks.
CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection.
{"title":"Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection","authors":"Adarsh Benjamin, Farha Fatina Wahid, Jenefa J","doi":"10.4108/eetpht.9.4376","DOIUrl":"https://doi.org/10.4108/eetpht.9.4376","url":null,"abstract":"INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images.
 OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored.
 METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures.
 RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks.
 CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"60 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver. It is a crucial organ essential for the proper functioning of the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, it regulates the balance of hormones in the body filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With this clinicians can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique.
肝脏位于腹部右上方，横膈膜的下方，胃的上方。它是人体正常运作所必需的重要器官。主要任务是消除我们的器官产生的废物，消化食物，保存维生素和能量物质。它在体内发挥着许多重要的功能，它调节体内激素的平衡，过滤和清除血液中的细菌、病毒和其他有害物质。在某些可怕的情况下，结果可能不幸导致死亡。根据病因或特征，肝脏疾病有多种分类。一些常见的肝脏疾病包括病毒性肝炎、自身免疫性肝病、代谢性肝病、酒精相关肝病、非酒精性脂肪性肝病、遗传性肝病、药物性肝损伤、胆道疾病。机器学习算法可以帮助识别人类可能难以察觉的模式和风险因素。有了这个，临床医生就可以早期诊断疾病，从而获得更好的治疗效果，改善患者护理。在这项研究工作中，不同类型的机器学习方法被实现，并在性能指标方面进行比较，以确定一个人是否受到影响。这里用于预测肝脏患者的算法有Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees classifier。实验结果表明，采用合成少数派过采样技术后，各种机器学习模型的准确率分别为随机森林分类器67.4%、k近邻分类器54.8%、xgboost分类器72%、决策树分类器65.1%、Logistic回归分类器68.0%、支持向量机65.1%、额外树分类器70.2%。
{"title":"Classification Algorithms for Liver Epidemic Identification","authors":"Koteswara Rao Makkena, Karthika Natarajan","doi":"10.4108/eetpht.9.4379","DOIUrl":"https://doi.org/10.4108/eetpht.9.4379","url":null,"abstract":"Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver. It is a crucial organ essential for the proper functioning of the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, it regulates the balance of hormones in the body filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With this clinicians can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"48 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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