A Multidomain Model for Dementia Classification using Harmonized LASI and LASI-DAD Data.
Shweta Anand1, Krishna P Miyapuram1
1Cognitive Science, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382055, Gujarat, India.
Medrxiv : the Preprint Server for Health Sciences
|July 3, 2026
Summary
Accurate dementia classification in diverse populations is improved by a multidomain model integrating cognitive, informant, and health data. This approach enhances prediction beyond cognitive tests alone, aiding early detection in heterogeneous groups.
Area of Science:
- Gerontology
- Epidemiology
- Biostatistics
Background:
- Dementia classification is challenging in diverse populations due to varying influences on cognitive tests.
- Existing methods using fixed thresholds may perform inconsistently across different groups.
Purpose of the Study:
- To develop and validate a multidomain classification model for dementia in a heterogeneous Indian cohort.
- To assess the incremental value of integrating cognitive, informant, cardiometabolic, and sociodemographic data.
Main Methods:
- Utilized harmonized data from the Longitudinal Ageing Study in India (LASI) and LASI-DAD.
- Defined dementia status using consensus-based Clinical Dementia Rating (CDR) assessments.
- Employed logistic regression, random forest, gradient boosting, XGBoost, and support vector machines with k-nearest neighbours imputation and SMOTE for class imbalance.
Main Results:
- The final logistic regression model achieved a ROC-AUC of 0.932 and average precision of 0.668.
- The multidomain model showed incremental discriminatory value over a cognition-only model (ROC-AUC 0.908).
- Informant-reported decline and orientation were key predictors, with cardiometabolic variables adding consistent contributions.
Conclusions:
- Integrating cognitive, informant, cardiometabolic, and sociodemographic information improves dementia classification in heterogeneous Indian populations.
- A single, interpretable model incorporating these diverse data types enhances predictive accuracy.
- Further external validation and calibration are needed before widespread application.
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