Abstract
Diabetes mellitus early detection is one of the most important issues in the literature nowadays. It contributes to the development of many deadly conditions, including heart disease, coronary disease, eye disease, kidney disease, and even nerve damage. As a result, its prediction is critical. Over the years, several academics have attempted to build an accurate diabetes prediction model. However, due to a lack of relevant data sets and prediction methodologies, this area still has substantial outstanding research concerns. The study attempts to solve the challenges by investigating healthcare predictive analytics. This project employs supervised learning through the application of 3 classification algorithms to early anticipate diabetes with high performance. To train and evaluate the prediction models, we used a sizable diabetes dataset based on actual health data gathered from the Centers for Disease Control and Prevention, which was properly pre-processed in this study, such as how the imbalance was handled utilizing resampling technique. We went with the Logistic Regression Algorithm, Decision Tree Algorithm, and Random Forest Algorithm to analyze the dataset. Based on several evaluation matrices, the results reveal that the RF algorithm outperformed other machine learning algorithms with an F1score of 93.01%. The results of the trial indicate that our suggested model outperforms cutting-edge alternatives. This study's findings may be useful to health professionals, organizations, students, and researchers working in diabetes prediction research and development.
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Samet, S., Laouar, R.M. (2023). Building Risk Prediction Models for Diabetes Decision Support System. In: Liu, S., Zaraté, P., Kamissoko, D., Linden, I., Papathanasiou, J. (eds) Decision Support Systems XIII. Decision Support Systems in An Uncertain World: The Contribution of Digital Twins . ICDSST 2023. Lecture Notes in Business Information Processing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32534-2_13
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