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

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,...

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Detalles Bibliográficos
Autores principales: Mishra, Sushruta, Tripathy, Hrudaya Kumar, Kumar Thakkar, Hiren, Garg, Deepak, Kotecha, Ketan, Pandya, Sharnil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718454/
https://www.ncbi.nlm.nih.gov/pubmed/34976936
http://dx.doi.org/10.3389/fpubh.2021.795007
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author Mishra, Sushruta
Tripathy, Hrudaya Kumar
Kumar Thakkar, Hiren
Garg, Deepak
Kotecha, Ketan
Pandya, Sharnil
author_facet Mishra, Sushruta
Tripathy, Hrudaya Kumar
Kumar Thakkar, Hiren
Garg, Deepak
Kotecha, Ketan
Pandya, Sharnil
author_sort Mishra, Sushruta
collection PubMed
description 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.
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spelling pubmed-87184542022-01-01 An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment Mishra, Sushruta Tripathy, Hrudaya Kumar Kumar Thakkar, Hiren Garg, Deepak Kotecha, Ketan Pandya, Sharnil Front Public Health Public Health 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. Frontiers Media S.A. 2021-12-17 /pmc/articles/PMC8718454/ /pubmed/34976936 http://dx.doi.org/10.3389/fpubh.2021.795007 Text en Copyright © 2021 Mishra, Tripathy, Kumar Thakkar, Garg, Kotecha and Pandya. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Mishra, Sushruta
Tripathy, Hrudaya Kumar
Kumar Thakkar, Hiren
Garg, Deepak
Kotecha, Ketan
Pandya, Sharnil
An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title_full An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title_fullStr An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title_full_unstemmed An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title_short An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
title_sort explainable intelligence driven query prioritization using balanced decision tree approach for multi-level psychological disorders assessment
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718454/
https://www.ncbi.nlm.nih.gov/pubmed/34976936
http://dx.doi.org/10.3389/fpubh.2021.795007
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