Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.40191
Thamizhvani T. R., Hemalatha R. J.
Alzheimer’s disease (AD) is a degenerative neuronal brain disorder resulting in memory loss, skills, and cognitive changes. The disorder’s primary diagnostic tests are defined as total brain atrophy and hippocampal atrophy. Early diagnosis is significant, and automatic systems design is necessary for this disorder. Potential biomarkers for AD are described using a hippocampal magnetic resonance imaging volumetry system with certain limitations. For the definite identification of the hippocampus region, pre-processing of the 3D MRI images of AD is necessary. The filtering and histogram-based pre-processing techniques enhance the region of interest, which helps in effectively segmenting the biomarker, the hippocampus. The median and eight histogram clippings are defined to be 98% efficient pre-processing techniques with the comparison of image quality parameters and statistical analysis. Thus an algorithm for pre-processing of the 3D MRI images of stages of AD is designed for the further process of identification.
{"title":"3D Pre-Processing Algorithm for MRI Images of Different Stages of AD","authors":"Thamizhvani T. R., Hemalatha R. J.","doi":"10.3991/ijoe.v19i15.40191","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.40191","url":null,"abstract":"Alzheimer’s disease (AD) is a degenerative neuronal brain disorder resulting in memory loss, skills, and cognitive changes. The disorder’s primary diagnostic tests are defined as total brain atrophy and hippocampal atrophy. Early diagnosis is significant, and automatic systems design is necessary for this disorder. Potential biomarkers for AD are described using a hippocampal magnetic resonance imaging volumetry system with certain limitations. For the definite identification of the hippocampus region, pre-processing of the 3D MRI images of AD is necessary. The filtering and histogram-based pre-processing techniques enhance the region of interest, which helps in effectively segmenting the biomarker, the hippocampus. The median and eight histogram clippings are defined to be 98% efficient pre-processing techniques with the comparison of image quality parameters and statistical analysis. Thus an algorithm for pre-processing of the 3D MRI images of stages of AD is designed for the further process of identification.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111512","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.41641
Tapasmini Sahoo, Kunal Kumar Das
A brain tumor is an abnormal collection of tissue in the brain. When tumors form, they are classified as either malignant or benign. It is critical to notice and identify the existence of tumors in brain images since they can be life threatening. This paper illustrates a novel segmentation method in which threshold technique is combined with normalized cut (Ncut) for the segregation of the tumors from brain magnetic resonance (MR) images. Image segmentation is a technique for grouping images. It is a method of splitting an image into sections with comparable attributes such as intensity, texture, colour, and so on. In thresholding, an object is distinguished from the background, and for the proposed segmentation methodology, the threshold value is determined by normalized graph cut. A weighted graph is divided into disjointed sets (groups) in which the similarity within a group is high and the similarity across groups is low. A graph-cut is a grouping approach in which the total weight of edges eliminated between these two pieces is used to calculate the degree of dissimilarity between these two groups. The normalized cut criterion is used to calculate the total likeness within the groups as well as the dissimilarity between the different groups.
{"title":"Brain Tumor Localization Using N-Cut","authors":"Tapasmini Sahoo, Kunal Kumar Das","doi":"10.3991/ijoe.v19i15.41641","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.41641","url":null,"abstract":"A brain tumor is an abnormal collection of tissue in the brain. When tumors form, they are classified as either malignant or benign. It is critical to notice and identify the existence of tumors in brain images since they can be life threatening. This paper illustrates a novel segmentation method in which threshold technique is combined with normalized cut (Ncut) for the segregation of the tumors from brain magnetic resonance (MR) images. Image segmentation is a technique for grouping images. It is a method of splitting an image into sections with comparable attributes such as intensity, texture, colour, and so on. In thresholding, an object is distinguished from the background, and for the proposed segmentation methodology, the threshold value is determined by normalized graph cut. A weighted graph is divided into disjointed sets (groups) in which the similarity within a group is high and the similarity across groups is low. A graph-cut is a grouping approach in which the total weight of edges eliminated between these two pieces is used to calculate the degree of dissimilarity between these two groups. The normalized cut criterion is used to calculate the total likeness within the groups as well as the dissimilarity between the different groups.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111731","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.43061
None Taif Nabeel Muslim, None Hassanain Ali Lafta
The hand of a human being is the most commonly utilized body part in daily activities. Assessing the functional capability is highly challenging and important in medical applications purposes. This research aims to design and implement a sensor-based system for function assessment and movements analysis of the hand by calculating the angular velocity, acceleration and magnetic field for the joints of the fingers during the daily activities. The proposed system was applied to two groups of volunteers: The first group consisted of seven males, whereas the second group consisted of seven females, and the results were taken by calculating the acceleration, angular velocity, magnetic field during activities of daily living (ADL). This study showed the system is important in hand movement and control function evaluation. The thumb and index fingers have similar pitch orientations while interacting, while the middle finger employs a distinct range. Yaw variables are less noticeable, but the variation in roll angles between fingers is.
{"title":"Modification of an IMU Based System for Analyzing Hand Kinematics During Activities of Daily Living","authors":"None Taif Nabeel Muslim, None Hassanain Ali Lafta","doi":"10.3991/ijoe.v19i15.43061","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.43061","url":null,"abstract":"The hand of a human being is the most commonly utilized body part in daily activities. Assessing the functional capability is highly challenging and important in medical applications purposes. This research aims to design and implement a sensor-based system for function assessment and movements analysis of the hand by calculating the angular velocity, acceleration and magnetic field for the joints of the fingers during the daily activities. The proposed system was applied to two groups of volunteers: The first group consisted of seven males, whereas the second group consisted of seven females, and the results were taken by calculating the acceleration, angular velocity, magnetic field during activities of daily living (ADL). This study showed the system is important in hand movement and control function evaluation. The thumb and index fingers have similar pitch orientations while interacting, while the middle finger employs a distinct range. Yaw variables are less noticeable, but the variation in roll angles between fingers is.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111508","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.43663
Umair Ali Khan, Ari Alamäki
Pain estimation in patients having communication difficulties is vital for preventing adverse consequences such as misdiagnosis, delayed treatment, and increased suffering. Traditional pain assessment tools relying on observer-based ratings and patient self-reporting are hampered by subjectivity and the need for continuous human monitoring, which have the potential to lead to inaccurate or delayed pain estimation. This paper presents an extensive literature review, a conceptual framework, and a systematic procedure for helping researchers develop a contactless, multimodal pain estimation system that leverages AI-based automation of standard pain assessment tools and scales within an AI sandbox environment. Our proposed concept aims to improve the efficiency of traditional pain estimation systems while reducing subjectivity and physical contact. This approach offers potential benefits, such as more accurate and timely pain assessment, reduced burden on healthcare professionals, and improved patient experiences. Moreover, the integration of the AI sandbox allows researchers and developers to experiment with AI models, algorithms, and systems safely and securely, ensuring that AI systems are reliable and robust before deployment. We also discuss potential challenges and ethical considerations related to the use of AI in pain estimation, emphasizing the importance of addressing these concerns to ensure the safe and responsible integration of this technology into healthcare systems. The paper lays a foundation for future research and innovation in pain management, ultimately contributing to better patient care and advancements in the field.
{"title":"Designing an Ethical and Secure Pain Estimation System Using AI Sandbox for Contactless Healthcare","authors":"Umair Ali Khan, Ari Alamäki","doi":"10.3991/ijoe.v19i15.43663","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.43663","url":null,"abstract":"Pain estimation in patients having communication difficulties is vital for preventing adverse consequences such as misdiagnosis, delayed treatment, and increased suffering. Traditional pain assessment tools relying on observer-based ratings and patient self-reporting are hampered by subjectivity and the need for continuous human monitoring, which have the potential to lead to inaccurate or delayed pain estimation. This paper presents an extensive literature review, a conceptual framework, and a systematic procedure for helping researchers develop a contactless, multimodal pain estimation system that leverages AI-based automation of standard pain assessment tools and scales within an AI sandbox environment. Our proposed concept aims to improve the efficiency of traditional pain estimation systems while reducing subjectivity and physical contact. This approach offers potential benefits, such as more accurate and timely pain assessment, reduced burden on healthcare professionals, and improved patient experiences. Moreover, the integration of the AI sandbox allows researchers and developers to experiment with AI models, algorithms, and systems safely and securely, ensuring that AI systems are reliable and robust before deployment. We also discuss potential challenges and ethical considerations related to the use of AI in pain estimation, emphasizing the importance of addressing these concerns to ensure the safe and responsible integration of this technology into healthcare systems. The paper lays a foundation for future research and innovation in pain management, ultimately contributing to better patient care and advancements in the field.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217629","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.41879
Hamza Abu Owida, Muhammad Al-Ayyad, Nidal M. Turab, Jamal I. Al-Nabulsi
Over the course of time, there has been a progression in the materials utilized for implants, transitioning from inert substances to those that replicate the structural characteristics of bone. Consequently, there has been a development of bioabsorbable, biocompatible, and bioactive materials. This article presents a comprehensive survey of diverse biomaterials with the potential to serve as scaffolds for bone tissue engineering. The objective of this study is to present an in-depth review of the predominant biomaterials utilized in the fabrication of scaffolds. This review encompasses the origins, classifications, characteristics, and methodologies involved in the development of these biomaterials. The review also highlights the incorporation of additives in biomaterial scaffolds. This study ultimately underscores the potential advantages and challenges associated with the utilization of biomaterials in scaffolds for bone tissue engineering. Additionally, it critically examines the integration of state-of-the-art technology with biomaterials.
{"title":"Recent Biomaterial Developments for Bone Tissue Engineering and Potential Clinical Application: Narrative Review of the Literature","authors":"Hamza Abu Owida, Muhammad Al-Ayyad, Nidal M. Turab, Jamal I. Al-Nabulsi","doi":"10.3991/ijoe.v19i15.41879","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.41879","url":null,"abstract":"Over the course of time, there has been a progression in the materials utilized for implants, transitioning from inert substances to those that replicate the structural characteristics of bone. Consequently, there has been a development of bioabsorbable, biocompatible, and bioactive materials. This article presents a comprehensive survey of diverse biomaterials with the potential to serve as scaffolds for bone tissue engineering. The objective of this study is to present an in-depth review of the predominant biomaterials utilized in the fabrication of scaffolds. This review encompasses the origins, classifications, characteristics, and methodologies involved in the development of these biomaterials. The review also highlights the incorporation of additives in biomaterial scaffolds. This study ultimately underscores the potential advantages and challenges associated with the utilization of biomaterials in scaffolds for bone tissue engineering. Additionally, it critically examines the integration of state-of-the-art technology with biomaterials.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111728","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.41969
Anitha T. Nair, Anitha M. L., Arun Kumar M. N.
Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future.
{"title":"Segmentation of Retinal Images Using Improved Segmentation Network, MesU-Net","authors":"Anitha T. Nair, Anitha M. L., Arun Kumar M. N.","doi":"10.3991/ijoe.v19i15.41969","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.41969","url":null,"abstract":"Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111725","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.43015
Khalid Fahad Alotaibi, None Azleena Mohd Kassim
The factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry.
{"title":"Factors That Influence the Adoption of Digital Dental Technologies and Dental Informatics in Dental Practice","authors":"Khalid Fahad Alotaibi, None Azleena Mohd Kassim","doi":"10.3991/ijoe.v19i15.43015","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.43015","url":null,"abstract":"The factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112121","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.42417
Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Hamadah Bader
Detection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Logistic Regression and Neural Network models perform the best on most criteria when considering all classes together. The SVM, Random Forest, and Naive Bayes models also receive moderate to high scores, suggesting their strength as classification models. However, the kNN and Tree models show poorer scores on most criteria across all classes, making them less favorable choices for this dataset. The SGD, AdaBoost, and CN2 rule inducer models perform the poorest when comparing all models using a weighted average of class scores. The results of the study suggest that machine learning algorithms may help predict the onset of diabetes and for detecting the disease at an early stage.
{"title":"Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods","authors":"Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Hamadah Bader","doi":"10.3991/ijoe.v19i15.42417","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.42417","url":null,"abstract":"Detection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Logistic Regression and Neural Network models perform the best on most criteria when considering all classes together. The SVM, Random Forest, and Naive Bayes models also receive moderate to high scores, suggesting their strength as classification models. However, the kNN and Tree models show poorer scores on most criteria across all classes, making them less favorable choices for this dataset. The SGD, AdaBoost, and CN2 rule inducer models perform the poorest when comparing all models using a weighted average of class scores. The results of the study suggest that machine learning algorithms may help predict the onset of diabetes and for detecting the disease at an early stage.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217889","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.42653
Abdeljalil El-Ibrahimi, Oumaima Terrada, Oussama El Gannour, Bouchaib Cherradi, Ahmed El Abbassi, Omar Bouattane
According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.
{"title":"Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction","authors":"Abdeljalil El-Ibrahimi, Oumaima Terrada, Oussama El Gannour, Bouchaib Cherradi, Ahmed El Abbassi, Omar Bouattane","doi":"10.3991/ijoe.v19i15.42653","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.42653","url":null,"abstract":"According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112472","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}
Pub Date : 2023-10-25DOI: 10.3991/ijoe.v19i15.40823
Oscar Boude, Carol Bravo Pineda
During the SARS-CoV-2 pandemic, many challenges were faced in the global healthcare system, one of which was the lack of competent professionals to implement therapies such as extracorporeal membrane oxygenation (ECMO), which proved to be lifesaving during the H1N1 virus infection. In response to this need, this project aimed to determine the characteristics of a blended training process to contribute to the development of competencies in the management of ECMO therapy and to understand the perception of participants regarding this training process as a suitable strategy for competency development. A mixed design with a descriptive scope based on design-based research was used. The main results indicated that the designed learning environment was suitable for competency development in ECMO therapy management, as well as the importance of including high-quality simulation scenarios in the development of skills for managing this type of therapy. However, the most significant impact was observed in the development of competencies and skills of the participating healthcare professionals through the process of feedback.
{"title":"Perfusionists’ Perception of a Blended Training Process in the Management of Extracorporeal Membranes","authors":"Oscar Boude, Carol Bravo Pineda","doi":"10.3991/ijoe.v19i15.40823","DOIUrl":"https://doi.org/10.3991/ijoe.v19i15.40823","url":null,"abstract":"During the SARS-CoV-2 pandemic, many challenges were faced in the global healthcare system, one of which was the lack of competent professionals to implement therapies such as extracorporeal membrane oxygenation (ECMO), which proved to be lifesaving during the H1N1 virus infection. In response to this need, this project aimed to determine the characteristics of a blended training process to contribute to the development of competencies in the management of ECMO therapy and to understand the perception of participants regarding this training process as a suitable strategy for competency development. A mixed design with a descriptive scope based on design-based research was used. The main results indicated that the designed learning environment was suitable for competency development in ECMO therapy management, as well as the importance of including high-quality simulation scenarios in the development of skills for managing this type of therapy. However, the most significant impact was observed in the development of competencies and skills of the participating healthcare professionals through the process of feedback.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217149","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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