Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification

Abstract

The global burden of Type II diabetes demands innovative strategies that combine predictive tools with targeted therapies. This study applies machine learning to the PIMA Indian dataset, identifying glucose, BMI, and age as key predictors, and integrates these with biological pathway mapping to support precision medicine. A novel methodological contribution is pathway mapping without gene-level data, linking clinical features to diabetes-related mechanisms like insulin signaling and PPAR pathways. This approach is particularly valuable for population datasets lacking molecular detail. In addition to established therapies (e.g., PPAR-based treatments, AMPK activators), the study explores emerging options such as dual GLP-1/GIP receptor agonists, novel AMPK activators, and sirtuin-related targets. Phytochemicals with multi-target effects are also evaluated. By bridging predictive modeling and biological insight, this research presents a framework for early detection and therapeutic innovation in T2DM, with special relevance for genetically predisposed populations such as the PIMA Indians.