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Predicting Gender Based on Severity of Symptoms of Schizophrenia and Cognitive Parameters

Predicting Gender Based on Severity of Symptoms of Schizophrenia and Cognitive Parameters

Kadir Uludag
Copyright: © 2024 |Pages: 12
ISBN13: 9798369324264|ISBN13 Softcover: 9798369366462|EISBN13: 9798369324271
DOI: 10.4018/979-8-3693-2426-4.ch014
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MLA

Uludag, Kadir. "Predicting Gender Based on Severity of Symptoms of Schizophrenia and Cognitive Parameters." Applications of Parallel Data Processing for Biomedical Imaging, edited by Rijwan Khan, et al., IGI Global, 2024, pp. 276-287. https://doi.org/10.4018/979-8-3693-2426-4.ch014

APA

Uludag, K. (2024). Predicting Gender Based on Severity of Symptoms of Schizophrenia and Cognitive Parameters. In R. Khan, I. Kumar, & P. Praveen (Eds.), Applications of Parallel Data Processing for Biomedical Imaging (pp. 276-287). IGI Global. https://doi.org/10.4018/979-8-3693-2426-4.ch014

Chicago

Uludag, Kadir. "Predicting Gender Based on Severity of Symptoms of Schizophrenia and Cognitive Parameters." In Applications of Parallel Data Processing for Biomedical Imaging, edited by Rijwan Khan, Indrajeet Kumar, and Pushkar Praveen, 276-287. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2426-4.ch014

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Abstract

Abstract Schizophrenia (SCZ) is a complex and chronic psychotic disorder characterized by a range of symptoms that affect the psychological well-being and stability of patients. These symptoms can be categorized into positive, negative, general, and cognitive domains. Goal:Our objective was to utilize a combination of symptoms exhibited by patients with SCZ and various cognitive variables to predict their gender. Methods: We recruited a total of twenty-three patients diagnosed with SCZ for our study. Results: Based on the primary findings of our study, the Random Forest (RF) model demonstrated a remarkable accuracy of 85.71% in predicting gender, with a sensitivity of 50% and specificity of 100%. Additionally, the Neural Networks (NN) model achieved an accuracy of 87.5% in the training set and 28.5% in the test set for gender prediction, with a sensitivity of 0% and specificity of 50%. Conversely, the Logistic Regression (LR) model exhibited lower performance, with an accuracy of 42.85%, sensitivity of 0%, and specificity of 60% in predicting gender.

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