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• W4230147780 abstract "<sec> <title>BACKGROUND</title> 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. A 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. The mental health problems are rising globally and constituting a burden on health-care 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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). </sec> <sec> <title>RESULTS</title> The proposed model generated the Term Frequency – Inverse Document Frequency (TF-IDF)-based average accuracy, precision, recall and F1 score of 67% thus outperforming other machine learning models namely, Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR) and Support Vector Machines (SVM). </sec> <sec> <title>CONCLUSIONS</title> 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, namely SPPM (RF+GBM). This proposed decision level fusion approach significantly improves the performance in terms of Accuracy, Recall, and F1-score with 67%, 66%, and 67% 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. Experiments revealed that unigram performed better on the experimental dataset. In the future, more physiological parameters such as respiratory rate, ECG, and EEG signals can be included as features to improve accuracy. Also, the proposed framework can be tested on a wide range of mental illness categories by adding more mental illness diseases in the dataset which will result in an increase of class labels. </sec>" @default.
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• W4230147780 date "2020-12-29" @default.
• W4230147780 modified "2023-09-27" @default.
• W4230147780 title "Decision Level Fusion using Ensemble Classifier for Mental Disease Classification (Preprint)" @default.
• W4230147780 doi "https://doi.org/10.2196/preprints.26826" @default.
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