Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Abstract

One out of every four individuals have a diagnosable mental disease in a given year. Social media is an excellent way for people to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may be used to anticipate people’s moods and emotions based on their postings and comments. On the other hand, psychometric tests use a series of questions to obtain information about how individuals think, feel, behave, and react. There is a necessity to investigate a hybrid approach for identifying people’s mental illness by combining social media inputs and psychometric tests, especially in the pandemic situation. Hence, the present paper aims at developing a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 per cent. By visiting a psychologist, the individual can use this result to take the required precautions.