Ensemble based Model for Diabetes Prediction and COVID-19 Mortality Risk Assessment in Diabetic Patients
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Abstract
Millions of individuals worldwide suffer from a chronic ailment known as diabetes, which can harm several organs, including the kidneys, eyes, and heart arteries. Diabetes patients are those who are most impacted by COVID-19. This study explores machine learning techniques for predicting diabetes and assessing COVID-19 mortality risks for diabetic patients. The study was conducted on diabetes and covid 19 datasets containing patients' demographic and clinical features. The machine learning techniques Support Vector Machine, Random Forest, Multilayer Perceptron, Naive Bayes, and Logistic Regression are used on both datasets. Additionally, their combination is employed to improve the performances of the models. These algorithms' accuracy, precision, recall, and F1-score were evaluated to determine their performance in predicting diabetes and COVID-19. By making this enhancement, healthcare services could use less time, labor, and resources while also making decisions with more reliability. The study results showed that the ensemble model had the highest accuracy in predicting diabetes and covid 19. The study also found that older age, higher HbA1c levels, and comorbidities significantly predict mortality risk in diabetic patients with COVID-19. The study concludes that machine learning techniques can help predict diabetes and assess COVID-19 mortality risks in diabetic patients. These findings could aid in developing effective prevention and treatment strategies for diabetes and COVID-19.
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