Systematic Literature Review: An Early Detection for Schizophrenia Classification Using Machine Learning Algorithms - Azizi, Ainin Sofiya; Kamal, Marnisha Mustafa; Azizan, Nurzarifah; Zawawi, Rohaizaazira Mohd; Zakaria, Noor Hidayah; Salamat, Mohamad Aizi; Yulherniwati, - (JOIV : International Journal on Informatics Visualization, 2024)
Schizophrenia is a complex mental health disorder that poses significant challenges in diagnosis and treatment due to its multifaceted symptoms, such as hallucinations, delusions, and cognitive impairments. Early detection is crucial for effective intervention, yet traditional diagnostic methods often fail in precision and scalability. This systematic literature review investigates the application of machine learning (ML) algorithms in the early detection and classification of schizophrenia. By synthesizing findings from 40 primary studies, the review highlights the effectiveness of diverse ML models, including Random Forests, Support Vector Machines (SVM), and advanced deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Key datasets such as clinical records, EEG signals, and neuroimaging data were analyzed to evaluate model performance across metrics like accuracy, precision, and sensitivity. Studies demonstrated that hybrid approaches, integrating multiple data sources and deep learning architectures, achieved classification accuracies exceeding 90%, with notable advancements in early-stage diagnosis. However, the review identifies critical challenges, including data quality issues, biases, and limited external validation, which hinder the widespread clinical application of these models. Through a comparative analysis of ML methods and traditional supervised approaches, the study underscores the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized treatment plans. Addressing current limitations, such as expanding data diversity and improving model interpretability, is essential for translating these findings into practical healthcare solutions. This research contributes to the growing knowledge in ML-driven diagnostics, advocating for its integration into clinical workflows to optimize schizophrenia management.