DEEP-STACK: Diabetes Prediction Using Stacked Model Approach

Diabetes, a cluster of metabolic diseases marked by persistently high blood sugar levels, presents significant health risks that can be mitigated through timely and precise diagnosis. The manuscript discusses a novel ensemble learning approach of early diabetes prediction, addressing the urgent need for enhanced diagnostic tools in contemporary healthcare. Our approach integrates advanced data processing techniques with a two-tier machine learning model to boost prediction accuracy. The proposed algorithm includes a thorough data preprocessing pipeline, encompassing missing value imputation, feature engineering, and dataset partitioning. The first tier consists of diverse base learners-Gradient Boosting, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors-that capture various data aspects. The second tier features a meta-learner, implemented as a Logistic Regression model that combines base learners' predictions. This ensemble technique harnesses the strengths of multiple algorithms to create a robust and precise diabetes prediction model. The experimental findings highlight the effectiveness of this method in early diabetes detection, potentially allowing healthcare professionals to intervene more promptly and efficiently. This research contributes to the ongoing efforts to enhance diabetes diagnosis and management, aiming to reduce the severity and complications linked to this chronic disease.