EARLY DETECTION AND PRECISE CLASSIFICATION OF TYPE 2 DIABETES THROUGH MACHINE LEARNING MODEL

Abstract

The present study investigated machine learning (ML) models to identify and classify type 2 diabetes and improve patient care through the early detection of diabetes-related complications. The research evaluates the effectiveness of five ML models-K-nearest neighbor (K-NN), Bernoulli Naive Bayes (BNB), decision trees (DT), logistic regression (LR), and support vector machines (SVM)-as well as three deep learning models-Res-Net, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)-in identifying individuals with diabetes. Using a Kaggle-hosted dataset that includes information from 2768 patients, both with and without diabetes, the study considers various factors such as previous pregnancies, blood glucose levels, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index, genetic background, and family history of diabetes, age, and outcome. The Res-Net model, with a success rate of 98%, emerged as the most effective in diabetes identification, closely followed by the MLP and LSTM models with success rates of 94.2% and 87.2%, respectively. In summary, the use of deep learning models for early diabetes detection shows significant promise, as evidenced by the favourable performance of the Res-Net model in this investigation.