Deep Learning Approach for Predicting Psychodiagnosis

Artificial intelligence methods, especially deep learning, have seen increasing application in analysing personality and occupational data to identify individuals with psychological and neurological disorders. Currently, there is a great need for effectively processing mental healthcare with the integration of artificial intelligence such as machine learning and deep learning. The paper addresses the pressing need for accurate and efficient methods for diagnosing psychiatric disorders, which are often complex and multifaceted. By exploiting the power of convolutional neural networks (CNN), we propose a novel CNN-based natural language processing method without removing stop words for predicting psychiatric diagnoses capable of accurately classifying individuals based on their psychological data. Our proposal is based on keeping a richer linguistic and semantic context to accurately predict psychiatric diagnosis. The experiment involves two datasets: one gathered from a private clinic and the other from Kaggle, called the Human Stress Dataset. The outcomes from the first dataset demonstrate a remarkable accuracy rate of 98.51% when employing CNN, showcasing their superior performance compared to the standard machine learning techniques such as logistic regression, k-nearest neighbours and support vector machines. With the second dataset, our model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.87. This result surpasses those achieved by existing state-of-the-art methods, further highlighting the efficacy of our CNN-based approach in discerning subtle nuances within the data and making accurate predictions. Moreover, we have compared our model with three other programs on the same dataset and the accuracy reached 78.52%. The results are promising to aid parents or clinicians in early and rapidly predicting the ill individual.