Pub Date : 2023-01-01DOI: 10.1504/ijeh.2023.10054122
Kalivaraprasanna Babu G, Thiyagarajan Paramasivan
{"title":"Broadening of horizons: A review of blockchains' influence on EHRs development trend","authors":"Kalivaraprasanna Babu G, Thiyagarajan Paramasivan","doi":"10.1504/ijeh.2023.10054122","DOIUrl":"https://doi.org/10.1504/ijeh.2023.10054122","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66761386","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 : 2023-01-01DOI: 10.1504/ijeh.2023.10055034
June Wei, Raquel Troccola
{"title":"Development of Usability Features for Mobile Nutrition","authors":"June Wei, Raquel Troccola","doi":"10.1504/ijeh.2023.10055034","DOIUrl":"https://doi.org/10.1504/ijeh.2023.10055034","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66761397","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 : 2023-01-01DOI: 10.1504/ijeh.2023.10059444
M. Hoq Chowdhury, Upol Chowdhury
{"title":"A Cough Type Chronic Disease Prediction Scheme Using Machine Learning and Diagnosis Support System Using a Mobile Application","authors":"M. Hoq Chowdhury, Upol Chowdhury","doi":"10.1504/ijeh.2023.10059444","DOIUrl":"https://doi.org/10.1504/ijeh.2023.10059444","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135838488","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 : 2023-01-01DOI: 10.1504/ijeh.2023.128605
Isaac Kofi Nti, Owusu Nyarko Boateng, Adebayo Felix Adekoya, Benjamin Asubam Weyori, Henrietta Pokuaa Adjei
Diabetes is a well-known risk factor for early mortality and disability. As signatories to the 2030 Agenda for Sustainable Development, Member States set an ambitious objective of a one-third reduction in early death due to non-communicable diseases (NCDs), which includes diabetes. Nonetheless, the current economic impact of diabetes on countries, individuals, and healthcare requires an agent means of its early detection. However, early detection of diabetes with conventional techniques is a considerable challenge for the healthcare industry and physicians. This study proposed a blended ensemble predictive model with Cohen's Kappa correlation-based base-learners selection to decrease unnecessary diabetes-related mortality through early detection. The empirical outcome shows that our proposed predictive model outperformed existing state-of-the-art approaches for predicting diabetes, thus resulting in enhanced diabetes prediction ability.
{"title":"Predicting diabetes using Cohen's Kappa blending ensemble learning","authors":"Isaac Kofi Nti, Owusu Nyarko Boateng, Adebayo Felix Adekoya, Benjamin Asubam Weyori, Henrietta Pokuaa Adjei","doi":"10.1504/ijeh.2023.128605","DOIUrl":"https://doi.org/10.1504/ijeh.2023.128605","url":null,"abstract":"Diabetes is a well-known risk factor for early mortality and disability. As signatories to the 2030 Agenda for Sustainable Development, Member States set an ambitious objective of a one-third reduction in early death due to non-communicable diseases (NCDs), which includes diabetes. Nonetheless, the current economic impact of diabetes on countries, individuals, and healthcare requires an agent means of its early detection. However, early detection of diabetes with conventional techniques is a considerable challenge for the healthcare industry and physicians. This study proposed a blended ensemble predictive model with Cohen's Kappa correlation-based base-learners selection to decrease unnecessary diabetes-related mortality through early detection. The empirical outcome shows that our proposed predictive model outperformed existing state-of-the-art approaches for predicting diabetes, thus resulting in enhanced diabetes prediction ability.","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470621","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 : 2023-01-01DOI: 10.1504/ijeh.2023.130515
Sanjeev Kumar, Harsh Tiwari, Mansi Jaiswal
Diabetes is one of the most severe and widespread diseases globally. It is also the cause of many ailments, including coronary artery disease, blindness, and urinary organ disorder. In this circumstance, patients must attend a diagnostic centre to obtain their reports after consultation. A range of methods is currently used to predict diabetes and diabetic-related illnesses. A diabetes forecasting model relying on machine learning recognises diabetes and provides more accurate results using several algorithms and optimisation strategies. It generates results relying on a collection of essential dataset parameters employed to train and test machine learning algorithms. Our proposed paper aims to design a system that can more accurately estimate a patient's diabetic risk level. Models are built using feature selection strategies, hyperparameter optimisation techniques, and essential classification techniques, including random forest and support vector machine. Our proposed scheme is more accurate and better than other existing diabetic-related schemes.
{"title":"Diabetes prediction using optimisation techniques with machine learning algorithms","authors":"Sanjeev Kumar, Harsh Tiwari, Mansi Jaiswal","doi":"10.1504/ijeh.2023.130515","DOIUrl":"https://doi.org/10.1504/ijeh.2023.130515","url":null,"abstract":"Diabetes is one of the most severe and widespread diseases globally. It is also the cause of many ailments, including coronary artery disease, blindness, and urinary organ disorder. In this circumstance, patients must attend a diagnostic centre to obtain their reports after consultation. A range of methods is currently used to predict diabetes and diabetic-related illnesses. A diabetes forecasting model relying on machine learning recognises diabetes and provides more accurate results using several algorithms and optimisation strategies. It generates results relying on a collection of essential dataset parameters employed to train and test machine learning algorithms. Our proposed paper aims to design a system that can more accurately estimate a patient's diabetic risk level. Models are built using feature selection strategies, hyperparameter optimisation techniques, and essential classification techniques, including random forest and support vector machine. Our proposed scheme is more accurate and better than other existing diabetic-related schemes.","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135637452","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 : 2022-01-01DOI: 10.1504/ijeh.2022.10041876
S. K, K. Saruladha
{"title":"An Effective Learning Rate Scheduler for Stochastic Gradient Descent Based Deep Learning Model in Healthcare Diagnosis System","authors":"S. K, K. Saruladha","doi":"10.1504/ijeh.2022.10041876","DOIUrl":"https://doi.org/10.1504/ijeh.2022.10041876","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66761328","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 : 2022-01-01DOI: 10.1504/ijeh.2022.10047823
Vengattaraman T, Thirumaran M, Ganesan M, Sivakumar N
{"title":"Internet of Medical Things and Cloud Enabled Brain Tumor Diagnosis Model using Deep Learning with Kernel Extreme Learning Machine","authors":"Vengattaraman T, Thirumaran M, Ganesan M, Sivakumar N","doi":"10.1504/ijeh.2022.10047823","DOIUrl":"https://doi.org/10.1504/ijeh.2022.10047823","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66761337","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 : 2022-01-01DOI: 10.1504/ijeh.2022.119583
Olayemi Olawumi, S. Olaleye, F. Adusei-Mensah, A. Olawuni, R. Agjei
{"title":"Adoption and implementation of electronic healthcare management system - a bibliometric approach","authors":"Olayemi Olawumi, S. Olaleye, F. Adusei-Mensah, A. Olawuni, R. Agjei","doi":"10.1504/ijeh.2022.119583","DOIUrl":"https://doi.org/10.1504/ijeh.2022.119583","url":null,"abstract":"","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"12 1","pages":"54-96"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66761348","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 : 2021-08-10DOI: 10.1504/ijeh.2021.10039422
G. M. Reddy, S. Gunda, Prasad Kompalli, Priyanka Gollapalli, A. Sevagamoorthy
Access to quality healthcare still remains a distant dream for significant proportion of global population. Direct virtual doctor consultation is emerging as potential solution to improve healthcare access. The current review was conducted to summarise the nature and impact of various virtual healthcare models reported in peer-reviewed scientific journals. We have also reviewed user and provider perspectives and attempted to present a critical analytical input to relevant stakeholders. The current study was a qualitative review of published studies retrieved from PubMED, Embase, Cochrane, and CINHAL plus. Virtual doctor consultation is not a new phenomenon and has been in practice for about the last few decades. Massive increase in the scale of adaptation in recent times makes it more palpable and is responsible for spurious perception of it as a recent phenomenon. Current technological advancements and better quality of data transfer makes it more effective and safer than ever before.
{"title":"VIRTUAL DOCTOR CONSULTATION, POTENTIAL TO REVOLUTIONIZE HEALTH CARE ACCESS IN RESOURCE POOR SETTINGS: OPPORTUNITIES AND CHALLENGES","authors":"G. M. Reddy, S. Gunda, Prasad Kompalli, Priyanka Gollapalli, A. Sevagamoorthy","doi":"10.1504/ijeh.2021.10039422","DOIUrl":"https://doi.org/10.1504/ijeh.2021.10039422","url":null,"abstract":"Access to quality healthcare still remains a distant dream for significant proportion of global population. Direct virtual doctor consultation is emerging as potential solution to improve healthcare access. The current review was conducted to summarise the nature and impact of various virtual healthcare models reported in peer-reviewed scientific journals. We have also reviewed user and provider perspectives and attempted to present a critical analytical input to relevant stakeholders. The current study was a qualitative review of published studies retrieved from PubMED, Embase, Cochrane, and CINHAL plus. Virtual doctor consultation is not a new phenomenon and has been in practice for about the last few decades. Massive increase in the scale of adaptation in recent times makes it more palpable and is responsible for spurious perception of it as a recent phenomenon. Current technological advancements and better quality of data transfer makes it more effective and safer than ever before.","PeriodicalId":39775,"journal":{"name":"International Journal of Electronic Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43645354","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：