Assessing the Feasibility of a Machine Learning-Based Diabetes Prediction Framework for e-Health Systems

In the global landscape of health concerns, diabetes stands out as a pervasive chronic illness marked by elevated blood sugar levels. Its unchecked progression poses significant risks, including cardiovascular complications, visual impairment, gastrointestinal issues, oral health problems, neuropathy, kidney disorders, hypertension, and disruptions in internal bodily functions. This research manuscript delves into the realm of diabetes diagnosis, employing advanced machine-learning techniques. The study meticulously evaluates twelve distinct algorithms, encompassing Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), MLP Classifier (MLP), Gradient Boosting Classifier (GB), AdaBoost Classifier (ADA), Naïve Bayes (NB), Cat Boost Classifier (CAT), Gaussian Process Classifier, and Light GBM (LGBM). The research not only investigates the predictive models but also explores the fundamental mechanisms leading to diabetes development. By leveraging a fusion of machine learning and ensemble methodologies, our objective is to accurately forecast diabetes, thereby enabling early intervention and effective disease management. The outcomes underscore the exceptional performance of the Light Gradient Boosting technique, achieving an impressive accuracy rate of 99%. Through an exhaustive comparative analysis, Light Gradient Boosting emerges as the standout method, surpassing other machine learning strategies in terms of accuracy. This research significantly advances our understanding of diabetes prediction utilizing machine learning and emphasizes the critical importance of early detection in managing this debilitating condition. The insights presented herein pave the way for the development of more robust and precise diabetes prediction models, with the potential to positively influence clinical decision-making and enhance patient outcomes.