Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations
Abstract One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-03-01
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| Series: | IET Communications |
| Online Access: | https://doi.org/10.1049/cmu2.12338 |
| _version_ | 1797990911688310784 |
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| author | Mahendra Kumar Gourisaria Gaurav Jee G. M. Harshvardhan Vijander Singh Pradeep Kumar Singh Tewabe Chekole Workneh |
| author_facet | Mahendra Kumar Gourisaria Gaurav Jee G. M. Harshvardhan Vijander Singh Pradeep Kumar Singh Tewabe Chekole Workneh |
| author_sort | Mahendra Kumar Gourisaria |
| collection | DOAJ |
| description | Abstract One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system. |
| first_indexed | 2024-04-11T08:43:07Z |
| format | Article |
| id | doaj.art-624c87bddfc340c1b96ef616197138ff |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-11T08:43:07Z |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-624c87bddfc340c1b96ef616197138ff2022-12-22T04:34:02ZengWileyIET Communications1751-86281751-86362022-03-0116553254710.1049/cmu2.12338Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nationsMahendra Kumar Gourisaria0Gaurav Jee1G. M. Harshvardhan2Vijander Singh3Pradeep Kumar Singh4Tewabe Chekole Workneh5School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha IndiaSchool of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha IndiaSchool of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha IndiaDepartment of Computer Science and Engineering School of Computing and Information Technology Manipal University Jaipur Rajasthan IndiaDepartment of Computer Science KIET Group of Institutions Ghaziabad Uttar Pradesh IndiaAdmas University EthiopiaAbstract One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system.https://doi.org/10.1049/cmu2.12338 |
| spellingShingle | Mahendra Kumar Gourisaria Gaurav Jee G. M. Harshvardhan Vijander Singh Pradeep Kumar Singh Tewabe Chekole Workneh Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations IET Communications |
| title | Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| title_full | Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| title_fullStr | Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| title_full_unstemmed | Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| title_short | Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| title_sort | data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations |
| url | https://doi.org/10.1049/cmu2.12338 |
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