An Ensemble Machine Learning Model for Automatic Prediction of Perceived Personal Well-Being of Indian University Students During COVID-19 Lockdown
DOI:
https://doi.org/10.52634/mier/2022/v12/i2/2280Keywords:
Perceived Personal Well-Being, Support Vector Regressor, Decision Tree Regressor, Lasso Regressor, Ensemble Model, COVID-19Abstract
COVID-19 has impacted personal well-being globally in a disruptive manner. Frequent lockdowns have slowed down dramatically the economy of every nation. There is a fear of future insecurity cropping up in the minds of the people. The paper aims to restructure the popular Personal Well-being Index (PWI) according to the relevant indicators that impacted students’ life in India during the second wave of COVID-19. The students at Delhi state university participated in the research. The researchers use various machine learning algorithms such as Lasso Regressor (LR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR) to predict the perceived PWI. The R-squared value for LR, SVR and DTR are 0.9103, 0.9159 and 0.5339. Mean squared errors are 0.0034, 0.0035 and 0.0105 respectively. The five most influential determinants of perceived PWI were extracted. An ensemble model of the three mentioned base learners was designed to remove the overfitting and underfitting problems. The algorithm has demonstrated impressive performance, with an R-squared value of 0.9839 and MSE of 0.0014. A GUI-based prediction model was implemented in Python that triggered the ensemble model at the back end to predict PWI based on five questions only, along with recommendations for the respondents.
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Copyright (c) 2022 Dr Kavita Pabreja, Shubham Arya, Parichay Madnani
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References
Ahuja, R., & Banga, A. (2019). Mental stress detection in university students using machine. Procedia Computer Scienc, 152, 349-353. https://doi.org/10.1016/j.procs.2019.05.007
Badawi, O., Brennan, T., Celi, L. A., Feng, M., Ghassemi, M., Ippolito, A., ... Zimolzak, A. (2014). Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference. JMIR MedicalInformatics, 2(2). https://doi.org/10.2196/medinform.3447
Baltatescu, S., & Cummins, R. (2006). Using the Personal Wellbeing Index to explore subjective wellbeing of high-school and college students in Romania. In 7th ISQOLS Conference: “Prospects for Quality of Life in the New Millennium” Grahamstown.
Beaumont, J., & Lofts, H. (2013). Measuring national well-being - health, 2013. Office for National Statistics.
Bonaccorso, G. (2017). Machine learning algorithms. Packt Publishing Ltd.
Chen, Z., & Davey, G. (2008). Normative life satisfaction in Chinese societies. Social Indicators Research, 89(3), 557-564.
Cummins, R. A. (2013). Personal wellbeing index-adult (pwi-a). Melbourne: Centre on Quality of Life, Deakin University.
Cummins, R. A. (2018). The golden triangle of happiness: Essential resources for a happy family. International Journal of Child, Youth and Family Studies, 9(4), 12-39. https://doi.org/10.18357/ijcyfs94201818638
Flesia, L., Monaro, M., Mazza, C., Fietta, V., Colicino, E., Segatto, B., & Roma, P. (2020). Predicting perceived stress related to the covid-19 outbreak through stable psychological traits and machine learning models. Journal of Clinical Medicine, 9(10), 1-17. https://doi.org/10.3390/jcm9103350
Francis, L. J. (2014). Oxford Happiness Questionnaire. Encyclopaedia of Quality of Life and Well-Being Research, 4548-4551. https://doi.org/10.1007/978-94-007-0753-5_4071
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Torresen, J. (2018). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing,51,1-26. https://doi.org/10.1016/j.pmcj.2018.09.003
Ghaderi, A., Frounchi, J., & Farnam, A. (2015). Machine learning-based signal processing using physiological signals for stress detection. In 22nd iranian conference on biomedical engineering (icbme) (p. 93-98). IEEE.
Haver, A., Akerjordet, K., Caputi, P., Furunes, T., & Magee, C. (2015). Measuring mental well-being: A validation of the short Warwick–Edinburgh mental well-being scale in Norwegian and Swedish. Scandinavian journal of public health, 43(7), 721-727.
Hermanns, N. (2007). WHO-5-Well-Being-Index. Der Diabetologe, 3, 464- 465. https://doi.org/10.1007/s11428-007-0179-2
Jones, S. S., Heaton, P. S., Rudin, R. S., & Schneider, E. C. (2012). Unraveling the IT Productivity Paradox - Lessons for Health Care. New England Journal of Medicine, 366(24), 2243-2245. https://doi.org/10.1056/nejmp1204980
Kaur, P., Sharma, M., & Mittal, M. (2018). Big Data and Machine Learning Based Secure Healthcare Framework. Procedia Computer Science, 132, 1049-1059. https://doi.org/10.1016/j.procs.2018.05.020
Keyes, C. L. M. (2009). Atlanta: Brief description of the mental health continuum short form (MHC-SF).
Lo-Ciganic, W. H., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., ... Gellad, W. F. (2019). Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA Network Open, 2(3). https://doi.org/10.1001/jamanetworkopen.2019.0968
Mcintyre, E., Saliba, A., & Mckenzie, K. (2020). Subjective wellbeing in the Indian general population: a validation study of the Personal Wellbeing Index. Quality of Life Research, 29(4), 1073-1081. https://doi.org/10.1007/s11136-019-02375-7
Michaelson, J., Mahony, S., & Schifferes, J. (2012). Measuring wellbeing: a guide for practitioners. London: New Economics Foundation. Misajon, R. A., Pallant, J., & Bliuc, A. M. (2016). Rasch analysis of the Personal Wellbeing Index. Quality of Life Research, 25(10), 2565-2569. https://doi.org/10.1007/s11136-016-1302-x
Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with python, scikit-learn, and tensorflow 2. Packt Publishing Ltd.
Uher, R., & Goodman, R. (2010). The everyday feeling questionnaire: the structure and validation of a measure of general psychological well- being and distress. Social psychiatry and psychiatric epidemiology, 45(3), 413-423.
UNICEF. (2021). The State of the World’s Children 2021; On My Mind: promoting, protecting and caring for children’s mental health. . Retrieved from https://www.unicef.org/india/press-releases/unicef-report-spotlights-mental-health-impact-covid-19-children-and-young-people
Van Beuningen, J., & De Jonge, T. (2011). Personal wellbeing index: Construct validity for the netherlands. Netherlands.
Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3), 457-469.
Watson, D., & Clark, L. A. (1994). Panas_X. FEBS Journal, 277(18), 3622- 3636.
Zhang, C., & Ma, Y. (2012). Ensemble machine learning: methods and applications. Springer Science & Business Media.
