Predicting Mental Health of Best Human Capital for Sustainable Organization through Psychological and Personality Health Issues: Shift from Traditional to Novel Machine Learning-Supervised Technique Approach.
Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human b...
| Published in: | BioMed Research International pp. 1 - 10 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
9/17/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159527757&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159527757 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/17/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159527757 159527757 159527757 10.1155/2022/5775640 159527757 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Mental Health of Best Human Capital for Sustainable Organization through Psychological and Personality Health Issues: Shift from Traditional to Novel Machine Learning-Supervised Technique Approach. aug: au: Khan, Muhammad Anees Ahmad, Sadique El-Affendi, Mohammed A. Zaka, Rija Mahmood, Saima Jehangir, Muhammad affil: Department of Management Studies, Bahria University, Islamabad, Pakistan sug: subj: Personnel Management Mental Health Sustainable Growth Machine Learning Methods Personality Psychological Well-Being Prediction Models Human Artificial Intelligence Support Vector Machine Decision Trees Logistic Regression Neural Networks (Computer) Organizational Development Job Performance ab: Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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