Work

Machine Learning Driven Insights into Socio-Economic, Demographic, and Health Determinants of Type-II Diabetes: A Tuned Hyperparameter Random Forest Model Using NFHS-5 Data

Year: 2026

Type: article

Abstract: Problem Considered: This investigation attempts to design and deploy a bespoke Random Forest model for early rate prediction of Type-II Diabetes utilizing the dataset available from NFHS-5. Methods: Hyperparameter optimization was carried out to boost the performance of a Random Forest algorithm. Missing value treatments and class imbalance problems were done utilizing SMOTE. Accuracy, recall, log loss and ROC-AUC were utilized to test the model's performance. Results: The accuracy, recall, log loss, and ROC-AUC score were 73.48%, 74.40%, 54.17%, and 81.17%, respectively. The study also indicates that demographic, lifestyle, comorbidities, and specific physiological characteristics significantly influence the prediction of diabetes. Conclusion: Random Forest algorithms are extremely effective with early diabetes detection and could help support the design of focused intervention and policy measures.

Source: Journal of Pharmacy And Bioallied Sciences

DOI: 10.4103/jpbs.jpbs_1612_25

Authors: Hitesh Athwani, Kuhu Awasthi, Meghna Athwani, Rajeev Pandey

Institutions: University of Lucknow, Bennett University, Government of Uttar Pradesh

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $250