TY  - JOUR
AU  - Choomung, Pichsinee
AU  - He, Yupeng
AU  - Matsunaga, Masaaki
AU  - Sakuma, Kenji
AU  - Kishi, Taro
AU  - Li, Yuanying
AU  - Tanihara, Shinichi
AU  - Iwata, Nakao
AU  - Ota, Atsuhiko
PY  - 2025
DA  - 2025/1/29
TI  - Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network–Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey
JO  - JMIR Form Res
SP  - e66330
VL  - 9
KW  - schizophrenia
KW  - schizophrenic
KW  - prevalence
KW  - artificial neural network
KW  - neural network
KW  - neural networks
KW  - ANN
KW  - deep learning
KW  - machine learning
KW  - SZ classifier
KW  - web-based survey
KW  - epidemiology
KW  - epidemiological
KW  - Japan
KW  - classifiers
KW  - mental illness
KW  - mental disorder
KW  - mental health
AB  - Background: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from hospitals and self-reported physician diagnoses or typical schizophrenia symptoms. These approaches are likely to underestimate the true prevalence owing to stigma, poor insight, or lack of access to health care among respondents. To address these issues, we previously developed an artificial neural network (ANN)–based schizophrenia classification model (SZ classifier) using data from a large-scale Japanese web-based survey to enhance the comprehensiveness of schizophrenia case identification in the general population. In addition, we also plan to introduce a population-based survey to collect general information and sample participants matching the population’s demographic structure, thereby achieving a precise estimate of the prevalence of schizophrenia in Japan. Objective: This study aimed to estimate the prevalence of schizophrenia by applying the SZ classifier to random samples from the Japanese population. Methods: We randomly selected a sample of 750 participants where the age, sex, and regional distributions were similar to Japan’s demographic structure from a large-scale Japanese web-based survey. Demographic data, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities were collected and applied to the SZ classifier, as this information was also used for developing the SZ classifier. The crude prevalence of schizophrenia was calculated through the proportion of positive cases detected by the SZ classifier. The crude estimate was further refined by excluding false-positive cases and including false-negative cases to determine the actual prevalence of schizophrenia. Results: Out of 750 participants, 62 were classified as schizophrenia cases by the SZ classifier, resulting in a crude prevalence of schizophrenia in the general population of Japan of 8.3% (95% CI 6.6%-10.1%). Among these 62 cases, 53 were presumed to be false positives, and 3 were presumed to be false negatives. After adjustment, the actual prevalence of schizophrenia in the general population was estimated to be 1.6% (95% CI 0.7%-2.5%). Conclusions: This estimated prevalence was slightly higher than that reported in previous studies, possibly due to a more comprehensive disease classification methodology or, conversely, model limitations. This study demonstrates the capability of an ANN-based model to improve the estimation of schizophrenia prevalence in the general population, offering a novel approach to public health analysis. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2025/1/e66330
UR  - https://doi.org/10.2196/66330
DO  - 10.2196/66330
ID  - info:doi/10.2196/66330
ER  -