An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment

Frontiers in Public Health

卷:9

Hiren Kumar Thakkar¹  Deepak Garg²  Sushruta Mishra³  Hrudaya Kumar Tripathy³  Ketan Kotecha⁴  Sharnil Pandya⁵ 

[1] Department of Computer Engineering, Marwadi Unversity, Rajkot, India;

[2] Department of Computer Science and Engineering, School of Engineering and Sciences, Bennett University, Greater Noida, India;

[3] School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India;

[4] Symbiosis Centre for Applied Artificial Intelligence, Symbosis International (Deemed) University, Pune, India;

[5] Symbiosis Institute of Technology, Symbosis International (Deemed) University, Pune, India;

关键词: psychological risks;    explainable intelligence;    decision tree;    oversampling;    predictive learning;   

DOI  :  10.3389/fpubh.2021.795007

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown