In-Silico Investigation of Computational Approaches for Schizophrenia Prediction
Abstract
Schizophrenia is a complex and debilitating mental disorder characterized by hallucinations, delusions, and cognitive impairments. Early detection and prediction are essential for effective intervention and improved patient outcomes. However, conventional machine learning methods face challenges in handling large, high-dimensional psychiatric datasets. To overcome these limitations, this study proposes a Quantum Machine Learning (QML)–based approach for schizophrenia prediction. By leveraging the computational capabilities of quantum computing, QML offers advanced techniques for analyzing complex data structures beyond the capacity of classical algorithms. This study evaluates the strengths and limitations of various quantum approaches and highlights appropriate evaluation metrics and validation strategies tailored for QML models. The findings provide a novel perspective on computational mental health diagnostics and underscore the potential of quantum computing in psychiatric prediction.
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