Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

TL;DR

This paper provides a comprehensive overview of machine learning methods for predicting Type 2 Diabetes and managing its complications, emphasizing real-world clinical applications and a framework aligned with physician workflows.

Contribution

It introduces an ML framework for T2DM prediction, risk stratification, intervention, and management, integrating clinical perspectives and addressing practical deployment challenges.

Findings

ML models effectively predict T2DM and complications.

Framework aligns with clinical decision-making processes.

Demonstrates real-world deployment considerations.

Abstract

Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.