A Comparative Machine Learning Framework for Early Diabetes Risk Prediction

Abstract

Diabetes is one of the leading causes of morbidity and mortality worldwide. To avoid difficult management of the condition, there is need to predict early onset of the condition. This study investigated the application of machine learning techniques and exploratory data analysis to forecast early-onset of diabetes using the PIMA Indian dataset. Preprocessing included handling missing values and standardization, leading to the development and evaluation of 5 models which include Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Neural Network. Exploratory analysis identified glucose concentration, body mass index, and age as the most influential features. Random Forest achieved the highest accuracy (0.74%) while both Random Forest and Logistic Regression attained the best ROC-AUC score of 0.81%. Feature importance analysis emphasized the predictive significance of glucose and BMI, aligning with clinical knowledge of diabetes risk factors. Despite the promising results the study acknowledged limitations related to the PIMA dataset's demographic scope and the moderate complexity of neural networks, highlighting areas for future enhancement. Ethical considerations, including data privacy and algorithmic bias, were addressed to ensure responsible model development.