Comparison of machine learning algorithms for predicting cognitive impairment using neuropsychological tests

According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

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Publication Details

Journal
Applied Neuropsychology Adult
Published
2024-09-09
DOI
https://doi.org/10.1080/23279095.2024.2392282
Citations
1
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.39

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article

Comparison of machine learning algorithms for predicting cognitive impairment using neuropsychological tests

Young Chul Youn, Chanda Simfukwe, Seong Soo An
1 citations
Applied Neuropsychology Adult
Dementia and Cognitive Impairment Research
0.39
article

Comparison of machine learning algorithms for predicting cognitive impairment using neuropsychological tests

Young Chul Youn, Chanda Simfukwe, Seong Soo An
article en
1 citations

Abstract

According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

Applied Neuropsychology Adult
Gachon University (KR), Chung-Ang University (KR)
National Research Foundation of Korea
Openalex Percentile: Top 36%
Dementia and Cognitive Impairment Research
0.39
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