Prediction of type 2 diabetes mellitus using medical attributes of the Leo SAC Polyclinic of San Juan de Lurigancho through the Machine Learning approach
DOI:
https://doi.org/10.53673/jb.v1i1.5Keywords:
predicción de diabetes, Machine Learning, datos del Policlínico Leo SAC de San Juan de LuriganchoAbstract
Deaths from diabetes increased by 70% globally between 2000 and 2019, ranking it among the top ten causes of mortality. It was the direct cause of 4.2 million deaths in 2019, and the number of adults (20-79 years) living with diabetes was approximately 463 million and is expected to rise to 700 million by 2045. Diabetes is a serious disease for health due to the presence of high glucose levels in the human body, so an early diagnosis will help treat it and prevent its complications. The need for an easy and fast way to diagnose diabetes is crucial. It is essential to evaluate the impacts of the chosen Machine Learning models using medical attributes, so we developed and tested 13 Machine Learning methods of classical models, neural networks and ensemble models to predict type 2 diabetes mellitus in elderly patients. the data set was obtained from the Leo SAC Polyclinic in San Juan de Lurigancho. Models with optimal hyperparameters were evaluated using the accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the training and test data set. In all seven performance measures, the model that consistently outperformed the others was LightGBM. This study demonstrates that the choice of Machine Learning models has an effect on the prediction results.
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