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Updated: Mar 20, 2026

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
Machine Learning Driven Insights into Socio-Economic, Demographic, and Health Determinants of Type-II Diabetes: A
Hitesh Athwani1, Kuhu Awasthi2, Meghna Athwani3
1Department of Statistics, University of Lucknow, Lucknow, Uttar Pradesh, India.
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.
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