J Korean Neuropsychiatr Assoc.  2017 Aug;56(3):103-110. 10.4306/jknpa.2017.56.3.103.

A Case Study of a Machine-Learning Approach in Differential Diagnosis of Schizophrenia: The Predictive Capacity of WAIS-IV

• Ko EH
• Kang HY
• Kim YS
• Jeong SH

• KMID: 2389393
• DOI: http://doi.org/10.4306/jknpa.2017.56.3.103

Abstract

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.

Keyword

Machine learning; Schizophrenia; WAIS-IV; Neuropsychological function; Diagnostic support system

MeSH Terms

Adult
Classification
Cognition Disorders
Data Collection
Diagnosis
Diagnosis, Differential*
Diagnostic and Statistical Manual of Mental Disorders
Humans
Intelligence
Machine Learning
Medical Records
Mental Disorders
Nonlinear Dynamics
Schizophrenia*

Figure

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  Fig. 1 Receiver operation curve depicting the model performances of the best nonlinear model (radial basis function kernel support vector machine) and the best linear model (penalized logistic regression). RBF-SVM : Radial basis function kernel support vector machine, Lasso : Penalized logistic regression, AUC : Area under the curve.

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