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Related Experiment Video

Updated: Jul 4, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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Published on: December 15, 2023

A Multidomain Model for Dementia Classification using Harmonized LASI and LASI-DAD Data.

Shweta Anand, Krishna P Miyapuram

    Medrxiv : the Preprint Server for Health Sciences
    |July 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    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.

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    Basics of Multivariate Analysis in Neuroimaging Data
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    Published on: July 24, 2010

    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.