Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark.

Mohammad Mahdi Ghiasi^1,2,3, Ryan Stanley Falck^1,2,3, Teresa Liu-Ambrose^2,3,4

¹School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.

PLOS Digital Health

|May 18, 2026

Summary

Related Concept Videos

Dementia l: Introduction

Dementia l: Introduction

Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Mapping women's health and well-being in Canada: A scoping review of findings from the Canadian longitudinal study on aging (CLSA).

Maturitas·2026

Same author

Use of Wearable Technology for Measuring and Characterizing Sedentary Behavior in People With Mild Cognitive Impairment and Dementia: Systematic Review.

JMIR aging·2026

Same author

Characterizing the role of menopausal status and hormone therapy on modifiable health behaviors and their association with body composition, physical function, and metabolic health.

Menopause (New York, N.Y.)·2026

Same author

Investigating Physical Exercise and Cognitive Training Effects on Cognition and Brain Health in Men and Women with Heart Failure: The ReCARDIO Trial.

CJC open·2026

Same author

Menopause and hormone therapy in relation to dietary intake, physical activity, and sleep, and meeting lifestyle guidelines.

Menopause (New York, N.Y.)·2026

Same author

Using case-mixes to understand health resource utilization trajectories among older adults at high risk of falls who received baseline geriatrician-based Falls Prevention Clinic care.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

This study establishes a leakage-free benchmark for dementia classification using longitudinal data, revealing lower realistic predictive performance after addressing methodological pitfalls. Robust models require careful data handling and feature engineering for accurate dementia prediction.

Area of Science:

Neuroscience
Medical Informatics
Machine Learning

Background:

Dementia research is hindered by methodological issues like data leakage, leading to overestimated model performance.
Prior studies often included target information (Clinical Dementia Rating - CDR) or used inappropriate data splitting for longitudinal datasets.
Temporal dynamics are inadequately incorporated in many dementia classification models.

Purpose of the Study:

To establish a transparent, leakage-free benchmark for dementia status classification using longitudinal data.
To address critical gaps in prior dementia research, including target leakage, information leakage, and inadequate temporal dynamics.
To evaluate the impact of engineered temporal features and interpretable AI on dementia classification.

Main Methods:

Related Experiment Videos

Last Updated: May 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Excluded CDR from predictor sets to prevent target leakage.
Implemented a group-aware data-splitting strategy for longitudinal data integrity.
Engineered temporal features and employed Support Vector Classifier (SVC) and LightGBM (LGBM) for classification.
Utilized Explainable AI (XAI) techniques (permutation importance, SHAP) for model interpretation.

Main Results:

SVC and LGBM, using combined original and engineered features, showed superior performance.
SVC achieved high precision (73.3%) and ROC AUC (89.1%); LGBM offered better recall (69.7%) and accuracy (69.7%).
XAI identified key predictors: MMSE and atlas-scaling-factor dynamics for SVC; education, age, and estimated total intracranial volume for LGBM. Models performed better in females.

Conclusions:

Eliminating data leakage significantly reduces dementia classification performance estimates, providing a more realistic benchmark.
Transparent and interpretable dementia classification models are crucial for robust longitudinal studies.
Further research is needed to address potential sex-based biases in dementia prediction models.