Abstract
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.
Highlights
According to the observation of WHO, a healthy mind with physical well-being is possessed by a healthy individual [1]
We present an explainable intelligence-enabled psychological disorders predictive model comprising a transparent and reliable intelligent model to detect acute mental disorders from accumulated online questionnaires through interactive sessions with patients
Using contrastive explanation method (CEM), we can improve the accuracy of the predictive model by analyzing the misclassified samples and subsequently processing those through CEM explanations
Summary
According to the observation of WHO, a healthy mind with physical well-being is possessed by a healthy individual [1]. Explainable Intelligence Driven Query Prioritization health concerns including anxiety, stress, and depression are common in people’s life Both personal and professional instabilities contribute to the occurrence of these disorders. Experts take the help of questionnaires and interactive sessions to assess these mental risks issues As per their observation, psychologically affected patients reserve their sentiments within themselves and are reluctant to share them with family, friends, or even medical professionals. Depression exhibits some vital factors like lack of focus, memory fluctuations, poor decision making, lack of zeal in recreation, feeling of helplessness and restlessness, weight loss, and suicidal mindset [6] As it is discussed, many common overlapping symptoms are observed in these three psychological risks like pain in the chest, insomnia, fatigue, pulse rate rise, and lack of concentration. A new approach called explainable intelligence needs to be adopted that are modeled to use predictive learning methods that perform the following functionalities
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