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March 3, 2026Current Bioinformatics

Leveraging Machine Learning to Assess Risk of Type 2 Diabetes and Cardiovascular Diseases in the North Indian Cohort

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Authors

SJSingh JyotsnaSam Higginbottom Institute of AgricultureTPTripathi PoojaSam Higginbottom Institute of AgricultureTVTripathi VijaySam Higginbottom Institute of Agriculture

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Overview

Cross-sectional analysis uses machine learning to predict diabetes and cardiovascular diseases risk in North Indians, highlighting significant health implications.

Key Points

  • This research aims to utilize machine learning to evaluate the risk of type 2 diabetes and cardiovascular diseases in a North Indian population.
  • Analyzed a cross-sectional dataset with clinical, biochemical, and lifestyle parameters.
  • Employed supervised machine learning algorithms such as SVM, Random Forest, Logistic Regression, and XGBoost.
  • Conducted feature normalization, correlation analysis, and hyperparameter tuning for model optimization.
  • Evaluated model performance using metrics like accuracy, precision, recall, F1-score, and AUC.
  • Machine learning models accurately identified individuals at high or low risk for T2DM and CVD.
  • Ensemble models like Random Forest and XGBoost showed superior performance compared to baseline algorithms.
  • The AdaBoost model achieved an AU-ROC score of 86.2% with non-laboratory data and 95.7% with laboratory data for diabetes prediction.
  • A Weighted Ensemble Model reached an AU-ROC of 83.1% with non-laboratory data for CVD prediction, improving to 93.7% with laboratory data.

Cite This Study

Jyotsna et al. (2026) studied this question.

synapsesocial.com/papers/69a67f12f353c071a6f0ae8bhttps://doi.org/10.2174/0115748936389285251125095534
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Investigation on Machine Learning Models for Predicting Diabetes Risk in Indian Populations2026
  2. 2Machine learning-based classification of diabetes mellitus using sociodemographic, behavioral, and clinical predictor2026
  3. 3Machine Learning-Based Prediction of Type 2 Diabetes in Indian Population2026
  4. 4Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition2026 · 2 citations
  5. 5Development and validation of a machine learning model for cardiovascular disease risk prediction in type 2 diabetes patients2025 · 17 citations