Mental Health Disorder Prediction Using Recurrent Neural Network Algorithm

Mental health disorders pose a significant challenge to healthcare professionals due to their complex and multifactorial nature. Early identification and accurate prediction of mental health disorders play a crucial role in timely intervention and effective treatment planning. The objective of this study is to explore the potential of deep learning algorithms in predicting mental health disorders, specifically focusing on depression, anxiety, and schizophrenia. The study utilizes a dataset containing clinical and demographic information of patients diagnosed with these disorders. The dataset was pre-processed using feature scaling algorithm, and features dimensionality reduction is done using t-Distributed Stochastic Neighbor Embedding (t-SNE) to train and evaluate deep learning models. These models were trained on large-scale datasets, allowing for the identification of complex patterns and relationships within the data. The training on dataset is done by using the Recurrent Neural Networks. The performance of the models was evaluated using standard evaluation metrics, such as accuracy, precision, recall, and F1-score. The results indicate that deep learning algorithms have shown promising results in predicting mental health disorders. The models achieved high accuracy rates, with some models surpassing the performance of traditional machine learning algorithms. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology.