The use of Machine Learning in diabetes prevention

Abstract

Introduction: Diabetes Mellitus is one of the fastest-growing chronic diseases globally. Machine Learning (ML) techniques offer significant potential for identifying patterns useful for disease control. Objective: To analyze the impact of ML techniques and the use of feature selection techniques in predicting diabetes, using the “Diabetes Health Indicators” dataset. Methods: The CRISP-DM methodology was applied. The data were balanced using the NearMiss subsampling technique. Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) were used for attribute selection. Six models were tested: Random Forest, Gradient Boosting, KNN, Logistic Regression, Multilayer Perceptron (MLP), and Recurrent Neural Networks (RNN). Results: Class balancing significantly improved results. The RNN achieved the best performance, with 86.8% accuracy and an F1-score of 0.868. The combination of RFE with MLP also showed strong performance. Feature selection (RFE and PCA) reduced dimensionality without loss of accuracy Conclusion: ML and DL techniques are promising for prioritizing clinical follow-up and informing public health policies. Enhancing data representativeness, integrating Explainable AI techniques, and adjusting thresholds to reduce false negatives are essential for practical applications.

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