A Lifestyle-Based Fuzzy-Enhanced ANN Model for Early Prediction of Type 2 Diabetes and Personalized Management in the North Indian Population.

Background

Type 2 Diabetes Mellitus (T2DM) continues to rise rapidly in Indian communities, affecting millions and posing a major public health challenge. Early identification of risk and timely lifestyle intervention are crucial for prevention. This study aims to develop a lifestyle-driven, fuzzy-enhanced Artificial Neural Network (ANN) model for early T2DM prediction and to design a personalized recommendation framework tailored to the North Indian population.

Methods

A comprehensive exploratory data analysis, including statistical significance testing and age-cohort assessment, was conducted to evaluate data quality and identify key lifestyle associations. The ANN model was trained on 1939 lifestyle profiles and classified individuals into four risk categories: low, moderate, high-risk, and diabetic. A monotonic spline-based calibration method was used to refine predicted probabilities. Additionally, a web-based system, the Personalized Care and Intelligence System for Early Diabetes Assessment (PCISEDA), was developed to deliver individualized diet and physical activity recommendations. Cost-effective lifestyle options were curated via a structured web-scraping pipeline.

Results

The proposed fuzzy-enhanced ANN model achieved an accuracy of 93.64%, precision of 94.00%, recall of 93.50%, F1-score of 93.50%, and a multiclass ROC-AUC of 94.07%, demonstrating strong discriminative performance. Feature importance analysis revealed age, weight, urination frequency, and thirst as the most influential lifestyle predictors of T2DM risk. The PCISEDA system successfully generated personalized and economically feasible lifestyle recommendations for each risk category.

Conclusions

This lifestyle-based AI framework demonstrates substantial potential for early T2DM risk stratification and tailored lifestyle management. The integration of fuzzy calibration and personalized recommendations offers an accurate, scalable, and cost-effective solution that may support diabetes prevention and management in resource-constrained healthcare settings.