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

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.