Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning models are highly prevalent for medical data analysis, disease diagnosis, and psychiatric nosology. This research addresses the challenge of detecting six major psychological disorders, namely, Anxiety, Bipolar Disorder, Conversion Disorder, Depression, Mental Retardation and Schizophrenia. These challenges are mined by applying decision level fusion of supervised machine learning algorithms. A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models. Furthermore, to reduce the impact of a conflicting decision, a voting scheme Shrewd Probing Prediction Model (SPPM) is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine (RF + GBM). This research provides an intuitive solution for mental disorder analysis among different target class labels or groups. A framework is proposed for determining the mental health problem of patients using observations of medical experts. The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique. This proposed decision level fusion approach by combining RF + GBM with SPPM-MIN significantly improves the performance in terms of Accuracy, Precision, Recall, and F1-score with 71\%, 73\%, 71\% and 71\% respectively. This framework seems suitable in the case of huge and more diverse multi-class datasets. Furthermore, three vector spaces based on TF-IDF (unigram, bi-gram, and tri-gram) are also tested on the machine learning models and the proposed model.