Predictive Modeling for Diabetes Mellitus: Evaluating Machine Learning Approaches on Big Data

Diabetes Mellitus is a chronic disease with significant global health and economic burdens, emphasizing the need for early diagnosis and effective management strategies. Predictive modeling, powered by machine learning (ML), offers a promising approach to identify at-risk individuals and support timely interventions. Leveraging big data, which encompasses vast and diverse healthcare information, enables the development of more accurate and comprehensive models. This paper evaluates various ML techniques, including traditional classifiers, ensemble methods, and deep learning algorithms, for predicting diabetes using large-scale datasets. Key challenges such as data preprocessing, feature selection, and class imbalance are addressed, while model performance is assessed using metrics like accuracy, precision, and AUC-ROC. The findings highlight the potential of ML to enhance predictive accuracy and identify critical predictors, contributing to personalized medicine and prevention strategies. Despite challenges in data quality, interpretability, and ethical considerations, this study underscores the transformative role of machine learning in diabetes prediction and the broader field of healthcare analytics.