Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting...
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Format: | Article |
Language: | English |
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MDPI AG
2024-03-01
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Series: | Applied Sciences |
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Online Access: | https://www.mdpi.com/2076-3417/14/5/2132 |
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author | Rejath Jose Faiz Syed Anvin Thomas Milan Toma |
author_facet | Rejath Jose Faiz Syed Anvin Thomas Milan Toma |
author_sort | Rejath Jose |
collection | DOAJ |
description | The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics. |
first_indexed | 2024-04-25T00:34:53Z |
format | Article |
id | doaj.art-e6aac99b0b034f10aa6a3a1acfa73300 |
institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-04-25T00:34:53Z |
publishDate | 2024-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Applied Sciences |
spelling | doaj.art-e6aac99b0b034f10aa6a3a1acfa733002024-03-12T16:40:13ZengMDPI AGApplied Sciences2076-34172024-03-01145213210.3390/app14052132Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and InterventionsRejath Jose0Faiz Syed1Anvin Thomas2Milan Toma3College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USACollege of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USACollege of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USACollege of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USAThe advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics.https://www.mdpi.com/2076-3417/14/5/2132PyCaretmachine learningstroke diagnosisdiagnostic accuracyautomated machine learninghealth informatics |
spellingShingle | Rejath Jose Faiz Syed Anvin Thomas Milan Toma Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions Applied Sciences PyCaret machine learning stroke diagnosis diagnostic accuracy automated machine learning health informatics |
title | Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions |
title_full | Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions |
title_fullStr | Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions |
title_full_unstemmed | Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions |
title_short | Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions |
title_sort | cardiovascular health management in diabetic patients with machine learning driven predictions and interventions |
topic | PyCaret machine learning stroke diagnosis diagnostic accuracy automated machine learning health informatics |
url | https://www.mdpi.com/2076-3417/14/5/2132 |
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