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Diagnosing Schizophrenia: A Deep Learning Approach

This paper presents a new method for diagnosing schizophrenia using deep learning. This experiment used a secondary dataset supplied by the National Institute of Health. The experiment analyzes the dataset and identifies schizophrenia using traditional machine learning methods such as logistic regression, support vector machines, and random forest. Finally, a deep neural network with three hidden layers is applied to the dataset. The results show that the neural network model yielded the highest accuracy, suggesting that deep learning may be a feasible method for diagnosing schizophrenia.