Application of machine learning models for predicting depression among older adults with non-communicable diseases in India.

ABSTRACT: Depression among older adults is a critical public health issue, particularly when coexisting with non-communicable diseases (NCDs). In India, where population ageing and NCDs burden are rising rapidly, scalable data-driven approaches are needed to identify at-risk individuals. Using data from the Longitudinal Ageing Study in India (LASI) Wave 1 (2017-2018; N = 58,467), the study evaluated eight supervised machine learning models including random forest, decision tree, logistic regression, SVM, KNN, naïve bayes, neural network and ridge classifier, for predicting depression among older adults. Model performance was assessed using a 70/30 train-test split and stratified 10-fold cross-validation. Performance evaluation metrics included AUROC, PR-AUC, accuracy, sensitivity, specificity, F1-sc

SUBMITTER: Yadav K 

PROVIDER: S-EPMC12494696 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature