A Case Study of a Machine-Learning Approach in Differential Diagnosis of Schizophrenia: The Predictive Capacity of WAIS-IV.
10.4306/jknpa.2017.56.3.103
- Author:
Eun Hae KO
1
;
Hi Yang KANG
;
Yong Sik KIM
;
Seong Hoon JEONG
Author Information
1. Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University, Daejeon, Korea. AnselmJeong@gmail.com
- Publication Type:Original Article
- Keywords:
Machine learning;
Schizophrenia;
WAIS-IV;
Neuropsychological function;
Diagnostic support system
- MeSH:
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*
- From:Journal of Korean Neuropsychiatric Association
2017;56(3):103-110
- CountryRepublic of Korea
- Language:Korean
-
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.
