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KMID : 0368820170560030103
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Journal of the Korean Neuropsychiatr Association
2017 Volume.56 No. 3 p.103 ~ p.110
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A Case Study of a Machine-Learning Approach in Differential Diagnosis of Schizophrenia: The Predictive Capacity of WAIS-IV
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Ko Eun-Hae
Kang Hi-Yang
Kim Yong-Sik
Jeong Seong-Hoon
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Abstract
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Objectives: Machine learning (ML) encompasses a body of statistical approaches that can detect complex interaction patterns from multi-dimensional data. ML is gradually being adopted in medical science, for example, in treatment response prediction and diagnostic classification. Cognitive impairment is a prominent feature of schizophrenia, but is not routinely used in differential diagnosis. In this study, we investigated the predictive capacity of the Wechsler Adult Intelligence Scale IV (WAIS-IV) in differentiating schizophrenia from non-psychotic illnesses using the ML methodology. The purpose of this study was to illustrate the possibility of using ML as an aid in differential diagnosis.
Methods: The WAIS-IV test data for 434 psychiatric patients were curated from archived medical records. Using the final diagnoses based on DSM-IV as the target and the WAIS-IV scores as predictor variables, predictive diagnostic models were built using 1) linear 2) non-linear/non-parametric ML algorithms. The accuracy obtained was compared to that of the baseline model built without the WAIS-IV information.
Results: The performances of the various ML models were compared. The accuracy of the baseline model was 71.5%, but the best non-linear model showed an accuracy of 84.6%, which was significantly higher than that of non-informative random guessing (p=0.002). Overall, the models using the non-linear algorithms showed better accuracy than the linear ones.
Conclusion: The high performance of the developed models demonstrated the predictive capacity of the WAIS-IV and justified the application of ML in psychiatric diagnosis. However, the practical application of ML models may need refinement and larger-scale data collection.
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KEYWORD
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Machine learning, Schizophrenia, WAIS-IV, Neuropsychological function, Diagnostic support system
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