Neuropsychological test using machine learning for cognitive impairment screening

Abstract

Objectives

Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models that detect normal controls (NC), cognitive impairment (CI), and dementia among subjects.

Patients and methods

A total number of 14,927 subject datasets were collected from the formal neuropsychological assessments Seoul Neuropsychological Screening Battery (SNSB) by well-qualified neuropsychologists. The dataset included 44 NPTs of SNSB, age, education level, and diagnosis of each participant. The dataset was preprocessed and classified according to three different classes NC, CI, and dementia. We trained machine-learning with a supervised machine learning classifier algorithm support vector machine (SVM) 30 times with classification from scikit-learn (https://scikit-learn.org/stable/) to distinguish the prediction accuracy, sensitivity, and specificity of the models; NC vs. CI, NC vs. dementia, and NC vs. CI vs. dementia. Confusion matrixes were plotted using the testing dataset for each model.

Results

The trained model's 30 times mean accuracies for predicting cognitive states were as follows; NC vs. CI model was 88.61 ± 1.44%, NC vs. dementia model was 97.74 ± 5.78%, and NC vs. CI vs. dementia model was 83.85 ± 4.33%. NC vs. dementia showed the highest accuracy, sensitivity, and specificity of 97.74 ± 5.78, 97.99 ± 5.78, and 96.08 ± 4.33% in predicting dementia among subjects, respectively.

Conclusion

Based on the results, the SVM algorithm is more appropriate in training models on an imbalanced dataset for a good prediction accuracy compared to natural network and logistic regression algorithms. The NC vs. dementia machine-learning trained model with SVM based on NPTs SNSB dataset could assist neuropsychologists in classifying the cognitive function of subjects.

Keywords:

• Cognitive impairment
• confusion matrix
• dementia disease
• machine learning
• support vector machine

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgments

We would like to thank the Department of Neurology at Chung-Ang University Hospital and Gachon University (Grant No. 2019-0357) for providing the tools to make this research successful.

Ethical approval

The ethics committee of the Department of Neurology, Chung-Ang University Hospital & Neurocognitive Behavior Center, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea approved this study.

Guarantor

C.S.

Author  contributions

C.S.: writing manuscript and methodology. Y.Y.C.: supervision, conceptualization, resources, manuscript edit, and review, funding, investigation, and project administration validation. S.K & S.S.A.: supervision, manuscript edit, review, and funding.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

The research was supported by the Ministry of Education of the Republic of Korea, and the National Research Foundation of Korea (NRF2017S1A6A3A01078538) and basic science research program through the national research foundation of Korea funded by the ministry of Education (2021R1A6A1A03038996).