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Prediction of cognitive impairment using higher order item response theory and machine learning models

Authors :
Lihua Yao
Yusuke Shono
Cindy Nowinski
Elizabeth M. Dworak
Aaron Kaat
Shirley Chen
Rebecca Lovett
Emily Ho
Laura Curtis
Michael Wolf
Richard Gershon
Julia Yoshino Benavente
Source :
Frontiers in Psychiatry, Vol 14 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Timely detection of cognitive impairment (CI) is critical for the wellbeing of elderly individuals. The MyCog assessment employs two validated iPad-based measures from the NIH Toolbox® for Assessment of Neurological and Behavioral Function (NIH Toolbox). These measures assess pivotal cognitive domains: Picture Sequence Memory (PSM) for episodic memory and Dimensional Change Card Sort Test (DCCS) for cognitive flexibility. The study involved 86 patients and explored diverse machine learning models to enhance CI prediction. This encompassed traditional classifiers and neural-network-based methods. After 100 bootstrap replications, the Random Forest model stood out, delivering compelling results: precision at 0.803, recall at 0.758, accuracy at 0.902, F1 at 0.742, and specificity at 0.951. Notably, the model incorporated a composite score derived from a 2-parameter higher order item response theory (HOIRT) model that integrated DCCS and PSM assessments. The study's pivotal finding underscores the inadequacy of relying solely on a fixed composite score cutoff point. Instead, it advocates for machine learning models that incorporate HOIRT-derived scores and encompass relevant features such as age. Such an approach promises more effective predictive models for CI, thus advancing early detection and intervention among the elderly.

Details

Language :
English
ISSN :
16640640
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Psychiatry
Publication Type :
Academic Journal
Accession number :
edsdoj.789acce75e99402a9c7e9c40736ecbdf
Document Type :
article
Full Text :
https://doi.org/10.3389/fpsyt.2023.1297952