Differential diagnosis of schizophrenia using decision tree analysis based on cognitive testing
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Wentian Dong [1] ; Yong He [1] ; Jiuju Wang [4] ; Chuan Shi [1] ; Qihui Niu [2] ; Haokui Yu [5] ; Jun Ji [3] ; Xin Yu [1]
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[1] Peking University
Peking University
China
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[2] First Affiliated Hospital of Zhengzhou University
First Affiliated Hospital of Zhengzhou University
China
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[3] Qingdao University
Qingdao University
China
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[4] Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
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[5] Beijing Wanling Pangu Technology Co., Ltd., Beijing, China
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- Localización: European journal of psychiatry, ISSN 0213-6163, Vol. 36, Nº 4, 2022, págs. 246-251
- Idioma: inglés
- Enlaces
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Resumen
Background and objectives To explore the discriminatory ability of a decision tree model based on cognitive testing data for the differential diagnosis of schizophrenia.
Methods This study enrolled 82 patients with schizophrenia and 82 patients with affective disorders. The cognitive function of the two groups of participants was assessed based on learning, symbol coding, digital span, trail making, and category fluency tests. The logistic regression model in the sklearn package in Python was applied to discriminate and analyse the data for all 11 variables in the MATRICS Consensus Cognitive Battery (MCCB).
Results The recognition rate for schizophrenia and affective disorder using all 11 variables of the MCCB was 82%.
Conclusion The logistics model based on cognitive data distinguished patients with schizophrenia from those with affective disorder.
