Increasing Schizophrenia Prediction Performance Using Advanced Deep Learning Learning Methodologies

Predicting schizophrenia is a difficult task that can tremendously benefit from machine learning. In this study, it
is suggested a thorough technique that includes data gathering, pre-processing, model building, training, and
evaluation. Convolutional neural networks (CNNs), Deep Neural Networks (DNNs), and Recurrent Neural
Networks (RNNs) are three Deep Learning architectures that were investigated for their ability to predict
schizophrenia. The findings indicated that RNNs are suitable for capturing temporal dependencies in patient
data, with the best accuracy rate of 0.87 being achieved in proposed study. Additionally, DNNs with more
extensive training data have greater development potential, while CNNs perform competitively according to F1-
Scores. CNNs regularly have higher precision values, demonstrating their dependability in reducing false
positives. This research's future focus is on validation and optimisation, which will guarantee its robustness for
clinical application. Interpretability can be improved by incorporating explainable AI (XAI) approaches.
Beyond diagnosing, these models can pinpoint those who are at danger, allowing for early interventions and
individualised treatment programmes. For effective application, collaboration with healthcare professionals,
ethical considerations, and data privacy are essential.