ArticleJournal of pharmacy & bioallied sciences2026
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.
Article in Journal of pharmacy & bioallied sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.