Diabetes is a kind of chronic disease, which refers to any illness that lasts for a longer period of time. It happens when the body’s blood sugar level is too high. Diabetes affects the elderly and obese in especially. Diabetic foot syndrome, heart attack, kidney failure, high blood pressure, and other health concerns can all be caused by diabetes. Machine learning is a vast field that learns from previous data and makes accurate predictions. Diabetes can be predicted early on, which can lead to better treatment. For this study, we used the Pima Indian Diabetes (PID) dataset from the UCI Machine Learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. We used five ML algorithms on the dataset to predict diabetes. The performance of all the five classifiers is compared using Accuracy score, Precision, Recall, F-measure Receiver Operating Curve (ROC) from each model and we observed that the Gradient Boosting techniques provide the Best Accuracy of 92.10% and ROC of 91.8%.
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ISSN 0973-4562 Volume 16
, Number 5
(2021
) pp. 410
–415
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