Yagnesh Challagundla, Badri Narayanan K, Krishna Sai Devatha, Bharathi V C, J. V. R. Ravindra
INTRODUCTION: In today's health-conscious world, accurate calorie monitoring during exercise is crucial for achieving fitness goals and maintaining a healthy lifestyle. However, existing methods often lack precision, driving the need for more reliable tracking systems. This paper explores the use of a multi-model machine learning approach to predict calorie burn during workouts by utilizing a comprehensive dataset. OBJECTIVES: The objective of this paper is to develop a user-friendly program capable of accurately predicting calorie expenditure during exercise, leveraging advanced machine learning techniques. METHODS: Techniques from social network analysis were employed to analyze the dataset, which included information on age, gender, height, weight, workout intensity, and duration. Data preprocessing involved handling missing values, eliminating irrelevant columns, and preparing features for analysis. The dataset was then divided into training and testing sets for model development and evaluation. Machine learning models, including Neural Networks, AdaBoost, Random Forest, and Gradient Boosting, were chosen based on their performance in regression tasks. RESULTS: The neural network model demonstrated superior performance in predicting calorie burn, outperforming other models in terms of MSE, RMSE, and an R2 score. Data visualization techniques aided in understanding the relationship between variables and calorie burn, highlighting the effectiveness of the neural network model. CONCLUSION: The findings suggest that a multi-model machine learning approach offers a promising solution for accurate calorie tracking during exercise. The neural network model, in particular, shows potential for developing user-friendly calorie monitoring applications. While limitations exist, such as dataset scope and environmental factors, this study lays the groundwork for future advancements in calorie monitoring and contributes to the development of holistic fitness applications.
{"title":"A Multi-Model Machine Learning Approach for Monitoring Calories Being Burnt During Workouts Using Smart Calorie Tracer","authors":"Yagnesh Challagundla, Badri Narayanan K, Krishna Sai Devatha, Bharathi V C, J. V. R. Ravindra","doi":"10.4108/eetpht.10.5407","DOIUrl":"https://doi.org/10.4108/eetpht.10.5407","url":null,"abstract":"INTRODUCTION: In today's health-conscious world, accurate calorie monitoring during exercise is crucial for achieving fitness goals and maintaining a healthy lifestyle. However, existing methods often lack precision, driving the need for more reliable tracking systems. This paper explores the use of a multi-model machine learning approach to predict calorie burn during workouts by utilizing a comprehensive dataset. \u0000OBJECTIVES: The objective of this paper is to develop a user-friendly program capable of accurately predicting calorie expenditure during exercise, leveraging advanced machine learning techniques. \u0000METHODS: Techniques from social network analysis were employed to analyze the dataset, which included information on age, gender, height, weight, workout intensity, and duration. Data preprocessing involved handling missing values, eliminating irrelevant columns, and preparing features for analysis. The dataset was then divided into training and testing sets for model development and evaluation. Machine learning models, including Neural Networks, AdaBoost, Random Forest, and Gradient Boosting, were chosen based on their performance in regression tasks. \u0000RESULTS: The neural network model demonstrated superior performance in predicting calorie burn, outperforming other models in terms of MSE, RMSE, and an R2 score. Data visualization techniques aided in understanding the relationship between variables and calorie burn, highlighting the effectiveness of the neural network model. \u0000CONCLUSION: The findings suggest that a multi-model machine learning approach offers a promising solution for accurate calorie tracking during exercise. The neural network model, in particular, shows potential for developing user-friendly calorie monitoring applications. While limitations exist, such as dataset scope and environmental factors, this study lays the groundwork for future advancements in calorie monitoring and contributes to the development of holistic fitness applications.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"2005 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246456","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: A thorough and timely investigation of any health-related problem is essential for disease prevention and treatment. The normal way of diagnosis may not be sufficient in the event of a serious illness problem. OBJECTIVE: Creating a medical diagnosis prototype that uses many machine learning processes to forecast any illness relied on symptoms explained by patients can lead to an errorless diagnosis as compared to the traditional ways. METHODS: We created a disease prediction prototype using ML techniques such as random forest, CART, multinomial linear regression, and KNN. The data set utilized for processing contained over 132 illnesses. Diagnosis algorithm outcomes the ailment that the person may be suffering from relied on the symptoms provided by the patients. RESULTS: When compared to CART and random forest (accuracy is 97.72%, multinomial linear regression and KNN produced the best outcomes. The accuracy of the KNN prediction and multinomial linear regression techniques was 98.76%. CONCLUSION: The diagnostic prototype can function as a doctor in the early detection of an illness, ensuring that medical care can begin in an appropriate time and many lives can be secured.
{"title":"A predictive prototype for the identification of diseases relied on the symptoms described by patients","authors":"S. K. Nayak, Mamata Garanayak, S. K. Swain","doi":"10.4108/eetpht.10.5405","DOIUrl":"https://doi.org/10.4108/eetpht.10.5405","url":null,"abstract":"INTRODUCTION: A thorough and timely investigation of any health-related problem is essential for disease prevention and treatment. The normal way of diagnosis may not be sufficient in the event of a serious illness problem. \u0000OBJECTIVE: Creating a medical diagnosis prototype that uses many machine learning processes to forecast any illness relied on symptoms explained by patients can lead to an errorless diagnosis as compared to the traditional ways. \u0000METHODS: We created a disease prediction prototype using ML techniques such as random forest, CART, multinomial linear regression, and KNN. The data set utilized for processing contained over 132 illnesses. Diagnosis algorithm outcomes the ailment that the person may be suffering from relied on the symptoms provided by the patients. \u0000RESULTS: When compared to CART and random forest (accuracy is 97.72%, multinomial linear regression and KNN produced the best outcomes. The accuracy of the KNN prediction and multinomial linear regression techniques was 98.76%. \u0000CONCLUSION: The diagnostic prototype can function as a doctor in the early detection of an illness, ensuring that medical care can begin in an appropriate time and many lives can be secured.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"221 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247129","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: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs. OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring. METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes. RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods. CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of reh
{"title":"Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes","authors":"Bin Wu","doi":"10.4108/eetpht.10.5150","DOIUrl":"https://doi.org/10.4108/eetpht.10.5150","url":null,"abstract":"INTRODUCTION: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs. OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring. METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes. RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods. CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of reh","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249763","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}
Colorectal cancer ranks as the third most common form of cancer in the United States. The Centres of Disease Control and Prevention report that males and individuals assigned male at birth (AMAB) have a slightly higher incidence of colon cancer than females and those assigned female at birth (AFAB) Black humans are more likely than other ethnic groups or races to develop colon cancer. Early detection of suspicious tissues can improve a person's life for 3-4 years. In this project, we use the EBHI-seg dataset. This study explores a technique called Generative Adversarial Networks (GAN) that can be utilized for data augmentation colorectal cancer histopathology Image Segmentation. Specifically, we compare the effectiveness of two GAN models, namely the deep convolutional GAN (DC-GAN) and the Variational autoencoder GAN (VAE-GAN), in generating realistic synthetic images for training a neural network model for cancer prediction. Our findings suggest that DC-GAN outperforms VAE-GAN in generating high-quality synthetic images and improving the neural network model. These results highlight the possibility of GAN-based data augmentation to enhance machine learning models’ performance in medical image analysis tasks. The result shows DC-GAN outperformed VAE-GAN.
结肠直肠癌是美国第三大常见癌症。美国疾病控制和预防中心（Centres of Disease Control and Prevention）报告称，男性和出生时被指定为男性（AMAB）的人患结肠癌的几率略高于女性和出生时被指定为女性（AFAB）的人。早期发现可疑组织可以延长患者 3-4 年的生命。在本项目中，我们使用了 EBHI-seg 数据集。本研究探索了一种称为生成对抗网络（GAN）的技术，该技术可用于数据增强型结直肠癌组织病理学图像分割。具体来说，我们比较了两种 GAN 模型（即深度卷积 GAN（DC-GAN）和变异自动编码器 GAN（VAE-GAN））在生成用于训练癌症预测神经网络模型的真实合成图像方面的有效性。我们的研究结果表明，在生成高质量合成图像和改进神经网络模型方面，DC-GAN 优于 VAE-GAN。这些结果凸显了基于 GAN 的数据增强技术在医学图像分析任务中提高机器学习模型性能的可能性。结果显示 DC-GAN 优于 VAE-GAN。
{"title":"Colorectal cancer prediction via histopathology segmentation using DC-GAN and VAE-GAN","authors":"R. Sujatha, Mahalakshmi K, M. Yoosuf","doi":"10.4108/eetpht.10.5395","DOIUrl":"https://doi.org/10.4108/eetpht.10.5395","url":null,"abstract":"Colorectal cancer ranks as the third most common form of cancer in the United States. The Centres of Disease Control and Prevention report that males and individuals assigned male at birth (AMAB) have a slightly higher incidence of colon cancer than females and those assigned female at birth (AFAB) Black humans are more likely than other ethnic groups or races to develop colon cancer. Early detection of suspicious tissues can improve a person's life for 3-4 years. In this project, we use the EBHI-seg dataset. This study explores a technique called Generative Adversarial Networks (GAN) that can be utilized for data augmentation colorectal cancer histopathology Image Segmentation. Specifically, we compare the effectiveness of two GAN models, namely the deep convolutional GAN (DC-GAN) and the Variational autoencoder GAN (VAE-GAN), in generating realistic synthetic images for training a neural network model for cancer prediction. Our findings suggest that DC-GAN outperforms VAE-GAN in generating high-quality synthetic images and improving the neural network model. These results highlight the possibility of GAN-based data augmentation to enhance machine learning models’ performance in medical image analysis tasks. The result shows DC-GAN outperformed VAE-GAN.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249690","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: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.
{"title":"Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices","authors":"Binbin Han, Fuliang Zhang, Lin Zhao","doi":"10.4108/eetpht.10.5163","DOIUrl":"https://doi.org/10.4108/eetpht.10.5163","url":null,"abstract":"INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"46 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250140","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}
Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.
{"title":"A Comprehensive Feature Engineering Approach for Breast Cancer Dataset","authors":"Shambhvi Sharma, Monica Sahni","doi":"10.4108/eetpht.10.5327","DOIUrl":"https://doi.org/10.4108/eetpht.10.5327","url":null,"abstract":"Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"40 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259301","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}
A. Pradeepa, O. V. Shanmuga Sundaram, N. Pushpalatha
INTRODUCTION: The goal of this research is to investigate child syndromes at the overall level using total edge magic labelling. luckily discussed with chromosomal diseases consisting of Down's syndrome, the syndrome of Edwards, and Patau syndrome. OBJECTIVES: Ultrasound is used to check for Patau's, Edwards, and Down syndrome between 11 and 14 weeks of pregnancy. These syndromes can be determined before the baby is born. The name for trisomy 21 or Down syndrome. Trisomy 18 or Edwards syndrome; trisomy 13 or Patau syndrome. METHODS: The ultrasound screen test was converted to a graphical image, and Total edge magic labelling was implemented. A bijection from VUE to the numbers, {1, 2, 3, … p+q} with the characteristic that each everybody uv Ɛ E, Γ(u)+ Γ(uv)+ Γ(v) = Ψ for some constant Ψ, is known as Total edge magic labelling. RESULTS: The results of this test will determine the baby’s type of trisomy. This study's impartial was to assess the efficacy of screening for 21, 18, and 13 trisomies at the 12-week mark in pregnancy. CONCLUSION: The intended audience of this paper is a man or woman with a chromosomal disorder who should know about the health of their ancestors. A couple can go for genetic counselling and then plan for a baby.
{"title":"Graphical image of Trisomy Ultrascan related Total edge magic labelling","authors":"A. Pradeepa, O. V. Shanmuga Sundaram, N. Pushpalatha","doi":"10.4108/eetpht.10.5311","DOIUrl":"https://doi.org/10.4108/eetpht.10.5311","url":null,"abstract":"INTRODUCTION: The goal of this research is to investigate child syndromes at the overall level using total edge magic labelling. luckily discussed with chromosomal diseases consisting of Down's syndrome, the syndrome of Edwards, and Patau syndrome. \u0000OBJECTIVES: Ultrasound is used to check for Patau's, Edwards, and Down syndrome between 11 and 14 weeks of pregnancy. These syndromes can be determined before the baby is born. The name for trisomy 21 or Down syndrome. Trisomy 18 or Edwards syndrome; trisomy 13 or Patau syndrome. \u0000METHODS: The ultrasound screen test was converted to a graphical image, and Total edge magic labelling was implemented. A bijection from VUE to the numbers, {1, 2, 3, … p+q} with the characteristic that each everybody uv Ɛ E, Γ(u)+ Γ(uv)+ Γ(v) = Ψ for some constant Ψ, is known as Total edge magic labelling. \u0000RESULTS: The results of this test will determine the baby’s type of trisomy. This study's impartial was to assess the efficacy of screening for 21, 18, and 13 trisomies at the 12-week mark in pregnancy. \u0000CONCLUSION: The intended audience of this paper is a man or woman with a chromosomal disorder who should know about the health of their ancestors. A couple can go for genetic counselling and then plan for a baby.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"13 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263007","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: Following the COVID-19 pandemic, telemedicine and telehealth have emerged as crucial technological resources for providing medical care and enhancing the competencies of healthcare professionals.OBJECTIVES: Analysing the implementation of Teletraining and TeleIEC in the healthcare services of Hospital II-2 Tarapoto in Peru.METHODS: A basic descriptive study with a mixed cross-sectional approach was conducted. The sample consisted of 266 healthcare specialist professionals and 4293 beneficiaries divided into three groups: healthcare personnel, healthcare students, and community members. The techniques employed included record analysis and surveys, with instruments consisting of a data registration form and a virtual questionnaire.RESULTS: In 2020, only 18% of professionals participated in teletraining and teleIEC activities. By August 2023, this figure had increased to 38%. It is also evident that the majority of professionals participating in these services as of 2023 were physicians (44%), followed by psychologists (16%), nurses (13%), and nutritionists (11%), reflecting limited participation from dentists (2%), obstetricians (1%), among others.CONCLUSION: The implementation of teletraining and teleIEC has a positive impact through the strengthening of competencies among professionals, students, and the general public, with learning levels reaching the second and third levels according to Bloom's taxonomy, namely comprehension and application.
{"title":"Analysis of the implementation of teletraining and teleIEC in healthcare services: Case study","authors":"Sarita Saavedra, Lloy Pinedo, Tamara Peña","doi":"10.4108/eetpht.10.5057","DOIUrl":"https://doi.org/10.4108/eetpht.10.5057","url":null,"abstract":"INTRODUCTION: Following the COVID-19 pandemic, telemedicine and telehealth have emerged as crucial technological resources for providing medical care and enhancing the competencies of healthcare professionals.OBJECTIVES: Analysing the implementation of Teletraining and TeleIEC in the healthcare services of Hospital II-2 Tarapoto in Peru.METHODS: A basic descriptive study with a mixed cross-sectional approach was conducted. The sample consisted of 266 healthcare specialist professionals and 4293 beneficiaries divided into three groups: healthcare personnel, healthcare students, and community members. The techniques employed included record analysis and surveys, with instruments consisting of a data registration form and a virtual questionnaire.RESULTS: In 2020, only 18% of professionals participated in teletraining and teleIEC activities. By August 2023, this figure had increased to 38%. It is also evident that the majority of professionals participating in these services as of 2023 were physicians (44%), followed by psychologists (16%), nurses (13%), and nutritionists (11%), reflecting limited participation from dentists (2%), obstetricians (1%), among others.CONCLUSION: The implementation of teletraining and teleIEC has a positive impact through the strengthening of competencies among professionals, students, and the general public, with learning levels reaching the second and third levels according to Bloom's taxonomy, namely comprehension and application.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263075","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}
S. Thangavel, Saravanakumar Selvaraj, Ganesh Karthikeyan V, K. Keerthika
INTRODUCTION: Preventable deaths from cardiovascular diseases outnumber all others combined. Detecting it at an early stage is crucial. Human lives will be saved as a result. OBJECTIVES: Improved cardiac disease prediction using machine learning classifiers is the focus of this article. METHODS: We have used many different classifiers, such as the support vector machine, naive bayes, random forest, and k-nearest neighbours, to achieve this goal, even though we can’t predict high accuracy in this classifier. So, we have proposed Hyper parameter adjustment was applied to the classifiers, which increased their precision. It was possible to compare the classifiers. RESULTS: In comparison to other machine learning classifiers, Logistic Regression achieves higher prediction accuracy, at 95.5%. CONCLUSION: To help people find the nearest cardiac care facilities, Google Maps has been integrated into a responsive web application that has been built for forecasting heart illness.
{"title":"Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease","authors":"S. Thangavel, Saravanakumar Selvaraj, Ganesh Karthikeyan V, K. Keerthika","doi":"10.4108/eetpht.10.5244","DOIUrl":"https://doi.org/10.4108/eetpht.10.5244","url":null,"abstract":"INTRODUCTION: Preventable deaths from cardiovascular diseases outnumber all others combined. Detecting it at an early stage is crucial. Human lives will be saved as a result.\u0000OBJECTIVES: Improved cardiac disease prediction using machine learning classifiers is the focus of this article.\u0000METHODS: We have used many different classifiers, such as the support vector machine, naive bayes, random forest, and k-nearest neighbours, to achieve this goal, even though we can’t predict high accuracy in this classifier. So, we have proposed Hyper parameter adjustment was applied to the classifiers, which increased their precision. It was possible to compare the classifiers.\u0000RESULTS: In comparison to other machine learning classifiers, Logistic Regression achieves higher prediction accuracy, at 95.5%.\u0000CONCLUSION: To help people find the nearest cardiac care facilities, Google Maps has been integrated into a responsive web application that has been built for forecasting heart illness.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411740","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}
Saravanakumar Selvaraj, S. Thangavel, M. Prabhakaran, T. Sathish
INTRODUCTION: Breast cancer is a major health concern for women all over the world. OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade. METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei. RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature. CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.
{"title":"Impressive predictive model for Breast Cancer based on Machine Learning","authors":"Saravanakumar Selvaraj, S. Thangavel, M. Prabhakaran, T. Sathish","doi":"10.4108/eetpht.10.5246","DOIUrl":"https://doi.org/10.4108/eetpht.10.5246","url":null,"abstract":"INTRODUCTION: Breast cancer is a major health concern for women all over the world. \u0000OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade. \u0000METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei. \u0000RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature. \u0000CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140409078","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：