Need of Machine Learning to Predict Happiness: A Systematic Review
Naveen1 and Bhatia, Anupam2
1Research Scholar, DCSA, Chaudhary Ranbir Singh University, Jind, Haryana, India
2Associate Professor, DCSA, Chaudhary Ranbir Singh University, Jind, Haryana, India
Abstract
Happiness is a current important subject of study in psychology and social science because it affects people’s day-to-day lives, thoughts and feelings, work habits, and interactions with society and family. There are a number of challenges in Computer Science and Machine Learning to predict happiness index using prediction techniques. This study presents a systematic review using PRISMA style for happiness prediction. During the Literature survey, it was found that many predictive models whether statistical or Machine Learning was designed to predict happiness index but a major emphasis on research remains focused on the factors that are listed in World Happiness Report, i.e., real Gross Domestic Product per capita, social support, healthy life expectancy, freedom to make life choices, generosity and perceptions of corruption. The factor influencing happiness varies due to personal differences, age group and location variation. According to Gallup Poll, the general annual sample for each country is 1,000 people i.e., approximately 0.007% population participated in happiness index measurement. The purpose of this study is to discover and describe new factors related to psychology like stress and emotions, location-based and age group. It is observed that there is a requirement to develop a Machine Learning predictive model which works on psychological factors like mental health, depression, stress, physical well-being, safety, leisure time available and suicidal ideation in addition to economic factors used in World Happiness Index and by targeting a large sample size of populations.
Keywords: Factors Affecting Happiness, Happiness Index, Machine Learning, Prediction Techniques.
Impact Statement
The past researches focus on the accuracy of predictive models which use different Machine Learning approaches to predict Happiness Index. Without knowing the impact of factors on happiness, the existing models are not useful for Phycologist, psychiatrists, Society and any other stakeholders. New factors that are related to social, psychological, environmental, etc. are never considered in Machine Learning approaches with World Happiness Report. The sample size taken by the World Happiness Report of a country is very small. World Happiness Report is used as a dataset only rather than analysing the factors affecting happiness.
There is a need to develop a Machine Learning model able to analyse maximum factors in a particular situation (geographical, academic, social, psychological, etc.) to predict the Happiness Index of a nation or of a targeted group so that society can be benefited.
About The Athor
Naveen completed her M.tech in Computer Science from Lovely Professional University, Punjab in 2015. She qualified NTA UGC NET examination in 2018. Till now, she has published 5 research papers. Now she is pursuing her PhD under the supervision of Dr Anupam Bhatia from Chaudhary Ranbir Singh University Jind.
Dr Anupam Bhatia completed his MCA in 2004 and PhD in 2013 from Kurukshetra University, Kurukshetra. He qualified UGC NET examination in December 2005. He did his PhD in the area of Data Mining using Genetic Algorithms. Till now, three research scholars have been awarded PhD under his supervision. Currently, five students are pursuing their PhD under his supervision. In addition to PhD, 7 MPhil and 4 MTech students have also done their dissertations under his supervision. His current research area is Machine Learning and Blockchain Technology. Most of his students research in interdisciplinary areas like Management, Psychology, and Computer Science. Till now he has published 24 research papers and attended 10 Conferences at the National and International Levels as a presenter, and session chair. He had been the Organizing Secretary of One National and One International Conference. He joined as Lecturer at erstwhile Kurukshetra University Post Graduate Regional Centre, Jind on 25th August 2007 as the first teacher of the institution. On 24 th July 2014, as per Chaudhary Ranbir Singh University, Act 2014; KUPGRC, Jind was elevated to Chaudhary Ranbir Singh University, Jind. Since, 25 th August 2021; He is serving as an Associate Professor, at the Department of Computer Science and Applications, Chaudhary Ranbir Singh University, Jind.
References
[1] “World mental health report: Transforming mental health for all.” https://www.who.int/publications/i/item/9789240049338 (accessed May 25, 2022).
[2] N. Salari et al., “Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis,” Global Health, vol. 16, no. 1, pp. 1–11, Jul. 2020, doi: 10.1186/S12992-020-00589-W/TABLES/2.
[3] R. P. Rajkumar, “COVID-19 and mental health: A review of the existing literature,” Asian J Psychiatr, vol. 52, p. 102066, Aug. 2020, doi: 10.1016/J.AJP.2020.102066.
[4] T. Agarwal, K. D. Mahato, C. Azad, and U. Kumar, “Predicting Happiness Score during Covid-19 Using Machine Learning,” AIST 2022 – 4th International Conference on Artificial Intelligence and Speech Technology, 2022, doi: 10.1109/AIST55798.2022.10065012.
[5] S. Sharma and Y. K. Gupta, “Predictive analysis and survey of COVID-19 using machine learning and big data,” https://doi.org/10.1080/09720502.2020.1833445, vol. 24, no. 1, pp. 175–195, 2021, doi: 10.1080/09720502.2020.1833445.
[6] M. A. Khder, M. A. Sayfi, and S. W. Fujo, “Analysis of World Happiness Report Dataset Using Machine Learning Approaches,” Int. J. Advance Soft Compu. Appl, vol. 14, no. 1, 2022, doi: 10.15849/IJASCA.220328.02.
[7] L. You, “Utilizing Machine Learning to Predict Happiness Index,” Proceedings – 2nd International Conference on E-Commerce and Internet Technology, ECIT 2021, pp. 233–238, Mar. 2021, doi: 10.1109/ECIT52743.2021.00058.
[8] N. S. Kakumanu, “AN OVERVIEW OF TRENDS AND TECHNIQUES IN PREDICTIVE ANALYTICS,” Journal of Contemporary Issues in Business and Government, vol. 28, no. 04, 2022, doi: 10.47750/cibg.2022.28.04.071.
[9] M. S. Purude, V. Narayanrao, P. Lalitha, and S. Kumari, “Analysis of Machine Learning Algorithms for Predicting Depression,” 2020.
[10] D. Aggarwal, V. Bali, and S. Mittal, “An insight into machine learning techniques for predictive analysis and feature selection,” International Journal of Innovative Technology and Exploring Engineering, vol. 8 Special Issue, no. 9, pp. 342–349, Jul. 2019, doi: 10.35940/ijitee.I1055.0789S19.
[11] L. Daniel-González, J. Moral-de la Rubia, A. Valle-de la O, M. L. Martínez-Martí, and C. H. García-Cadena, “A predictive model of happiness among medical students,” Current Psychology, vol. 42, no. 2, pp. 955–966, Jan. 2023, doi: 10.1007/s12144-021-01485-1.
[12] Y. Zhang, “Analyze and Predict the 2022 World Happiness Report Based on the Past Year’s Dataset,” Journal of Computer Science, vol. 19, no. 4, pp. 483–492, 2023, doi: 10.3844/jcssp.2023.483.492.
[13] Y. Tan, C. Singhapreecha, and W. Yamaka, “Applying Machine Learning to Predict Happiness: A case study of 20 Countries,” 2020.
[14] A. Chakraborty and C. P. Tsokos, “A Real Data-Driven Analytical Model to Predict Happiness A Real Data Driven Analytical Model to Predict Happiness,” 2021, doi: 10.21203/rs.3.rs-224834/v1.
[15] Z. Fan, F. Wu, and Y. Tang, “A hierarchy-based machine learning model for happiness prediction,” Applied Intelligence, vol. 53, no. 6, pp. 7108–7117, Mar. 2023, doi: 10.1007/S10489-022-03811-X/METRICS.
[16] S. Nikam, “Predicting World Happiness using Machine Learning,” International Journal of Scientific Research in Engineering and Management, 2021, Accessed: Jul. 14, 2023. [Online]. Available: www.ijsrem.com
[17] M. Lubis, D. O. D. Handayani, and A. R. Lubis, “Prediction analysis of the happiness ranking of countries based on macro level factors,” IAES International Journal of Artificial Intelligence, vol. 11, no. 2, pp. 666–678, Jun. 2022, doi: 10.11591/ijai.v11.i2.pp666-678.
[18] M. Kaur, M. Dhalaria, P. K. Sharma, and J. H. Park, “Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring,” Applied Sciences 2019, Vol. 9, Page 1613, vol. 9, no. 8, p. 1613, Apr. 2019, doi: 10.3390/APP9081613.
[19] Ö. Baltacı, “The Predictive Relationships between the Social Media Addiction and Social Anxiety, Loneliness, and Happiness,” International Journal of Progressive Education, vol. 15, no. 4, pp. 73–82, Aug. 2019, doi: 10.29329/ijpe.2019.203.6.
[20] D. Ramesh and Y. S. Katheria, “Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach,” Health Technol (Berl), vol. 9, no. 4, pp. 533–545, Aug. 2019, doi: 10.1007/S12553-019-00299-3/METRICS.
[21] F. Ibnat, J. Gyalmo, Z. Alom, M. A. Awal, and M. A. Azim, “Understanding World Happiness using Machine Learning Techniques,” 6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021, 2021, doi: 10.1109/IC4ME253898.2021.9768407.
[22] V. A. Athavale, “Application of machine learning to cluster countries based on dominant parameters of happiness index Security Issues in Cloud Computing: A holistic view View project,” 2021, doi: 10.26697/ijes.2021.3.1.
[23] P. Chaipornkaew and T. Prexawanprasut, “A Prediction Model for Human Happiness Using Machine Learning Techniques,” Proceeding – 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, pp. 33–37, Oct. 2019, doi: 10.1109/ICSITECH46713.2019.8987513.
[24] L. Musikanski et al., “Happiness Index Methodology,” Journal of Social Change, vol. 9, no. 1, pp. 4–31, 2017, doi: 10.5590/JOSC.2017.09.1.02.
[25] “The Science of Happiness in Positive Psychology 101.” https://positivepsychology.com/happiness/ (accessed Jan. 12, 2023).
[26] “World Happiness Index and Its Six Factors – 577 Words | Report Example.” https://ivypanda.com/essays/world-happiness-index-and-its-six-factors/ (accessed Jan. 26, 2023).
[27]“https://agingcapriciously.files.wordpress.com/2022/04/cnx_psych_14_05_happiness.jpg. – Bing images.” (Accessed Jan. 23, 2023).
[28] Z. Mahmoodi, M. Yazdkhasti, M. Rostami, and N. Ghavidel, “Factors affecting mental health and happiness in the elderly: A structural equation model by gender differences,” Brain Behav, vol. 12, no. 5, p. e2549, May 2022, doi: 10.1002/BRB3.2549.
[29] H. Sukmana, M. H. Karuniawan, T. W. Abadi, and I. U. Choiriyah, “Factors Affecting of Lifes Happiness,” Proceedings of the Annual Conference on Social Sciences and Humanities (ANCOSH 2018)-Revitalization of Local Wisdom in Global and Competitive Era, pp. 41–45, 2018, doi: 10.5220/0007410045.
[30] Y.-K. Ng, “Factors Affecting Happiness,” Happiness—Concept, Measurement and Promotion, pp. 99–114, 2022, doi: 10.1007/978-981-33-4972-8_10.
[31] H. K. Jo, H. K. Kim, and J. N. Jeong, “Factors Affecting Happiness Among Rural Residents: A Cross Sectional Survey,” Community Ment Health J, vol. 56, no. 5, pp. 915–924, Jul. 2020, doi: 10.1007/S10597-020-00555-1/METRICS.
[32] M. Azizi, F. Mohamadian, M. Ghajarieah, and A. Direkvand-Moghadam, “The Effect of Individual Factors, Socioeconomic and Social Participation on Individual Happiness: A Cross-Sectional Study,” J Clin Diagn Res, vol. 11, no. 6, p. VC01, Jun. 2017, doi: 10.7860/JCDR/2017/24658.9982.
[33] P. Oberoi, S. Chopra, and Y. Seth, “A Comparative Analysis Of The Factors Affecting Happiness Index”, Accessed: Jul. 14, 2023. [Online]. Available: www.ijstr.org
[34] A. Mehrdadi, S. Sadeghian, A. Direkvand-Moghadam, and A. Hashemian, “Factors Affecting Happiness: A Cross-Sectional Study in the Iranian Youth,” J Clin Diagn Res, vol. 10, no. 5, p. VC01, May 2016, doi: 10.7860/JCDR/2016/17970.7729.
[35] “FAQ | The World Happiness Report.” https://worldhappiness.report/faq/ (accessed Mar. 09, 2023).
[36] J. F. Helliwell, R. Layard, J. D. Sachs, J.-E. De Neve, L. B. Aknin, and S. Wang, “World Happiness Report 2023,” World Happiness Report, p. 166, 2023, Accessed: Mar. 30, 2023. [Online]. Available: https://worldhappiness.report/ed/2023/
[37] J. F. Helliwell, R. Layard, J. D. Sachs, J.-E. De Neve, L. B. Aknin, and S. Wang, “World Happiness Report 2021,” World Happiness Report, p. 212, 2021, Accessed: Mar. 09, 2023. [Online]. Available: https://worldhappiness.report/ed/2021/
[38] J. F. Helliwell, R. Layard, J. D. Sachs, J.-E. De Neve, L. B. Aknin, and S. Wang, “World Happiness Report 2022,” 2022, Accessed: Mar. 09, 2023. [Online]. Available: https://worldhappiness.report/ed/2022/
[39] J. F. Helliwell et al., “World Happiness Report 2020,” 2020, Accessed: Mar. 09, 2023. [Online]. Available: https://worldhappiness.report/ed/2020/
[40] B. Hutton et al., “The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions: Checklist and Explanations,” https://doi.org/10.7326/M14-2385, vol. 162, no. 11, pp. 777–784, Jun. 2015, doi: 10.7326/M14-2385.
[41] D. Moher et al., “Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement,” Revista Espanola de Nutricion Humana y Dietetica, vol. 20, no. 2, pp. 148–160, Jan. 2016, doi: 10.1186/2046-4053-4-1/TABLES/4.
[42] H. Rosenbusch, F. Soldner, A. M. Evans, and M. Zeelenberg, “Supervised machine learning methods in psychology: A practical introduction with annotated R code,” Soc Personal Psychol Compass, vol. 15, no. 2, p. e12579, Feb. 2021, doi: 10.1111/SPC3.12579.
[43] A. Thieme, D. Belgrave, and G. Doherty, “Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems,” ACM Transactions on Computer-Human Interaction (TOCHI), vol. 27, no. 5, p. 34, Aug. 2020, doi: 10.1145/3398069.
[44] M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science,” pp. 3–21, 2020, doi: 10.1007/978-3-030-22475-2_1.
[45] S. S. Dash, S. K. Nayak, and D. Mishra, “A review on machine learning algorithms,” Smart Innovation, Systems and Technologies, vol. 153, pp. 495–507, 2021, doi: 10.1007/978-981-15-6202-0_51/COVER.
[46] B. Mahesh, “Machine Learning Algorithms-A Review,” International Journal of Science and Research, 2018, doi: 10.21275/ART20203995.
[47] S. Kino et al., “A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects,” SSM Popul Health, vol. 15, p. 100836, Sep. 2021, doi: 10.1016/J.SSMPH.2021.100836.
[48] Z. Wang, “A Method of Evaluating the Happy Society,” pp. 1184–1189, Jun. 2017, doi: 10.2991/ICESAME-17.2017.258.
[49] S. Sujarwoto, G. Tampubolon, and A. C. Pierewan, “Individual and Contextual Factors of Happiness and Life Satisfaction in a Low Middle-Income Country,” Appl Res Qual Life, vol. 13, no. 4, pp. 927–945, Dec. 2018, doi: 10.1007/S11482-017-9567-Y/METRICS.
[50] O. N. Medvedev and C. E. Landhuis, “Exploring constructs of well-being, happiness and quality of life,” PeerJ, vol. 2018, no. 6, p. e4903, Jun. 2018, doi: 10.7717/PEERJ.4903/SUPP-1.
[51] “Factors-affecting-happiness-Source-Adapted-from-Layard-2011.png (850×532).” https://www.researchgate.net/profile/Siti-Kamaruddin-2/publication/290286392/figure/fig4/AS:671523228622855@1537115022244/Factors-affecting-happiness-Source-Adapted-from-Layard-2011.png (accessed Mar. 25, 2023).
[52] P. S. Rajawat, D. K. Gupta, S. S. Rathore, and A. Singh, “Predictive Analysis of Medical Data using a Hybrid Machine Learning Technique,” ICSCCC 2018 – 1st International Conference on Secure Cyber Computing and Communications, pp. 228–233, Jul. 2018, doi: 10.1109/ICSCCC.2018.8703302.
[53] M. Oyeleye, T. Chen, S. Titarenko, and G. Antoniou, “A Predictive Analysis of Heart Rates Using Machine Learning Techniques,” Int J Environ Res Public Health, vol. 19, no. 4, Feb. 2022, doi: 10.3390/IJERPH19042417.
[54] R. Chahar, A. K. Dubey, and S. Narang, “A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection,” Article in International Journal of Advanced Technology and Engineering Exploration, 2021, doi: 10.19101/IJATEE.2021.874198.
[55] P. T. Ogink et al., “Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review,” Acta Orthop, vol. 92, no. 5, pp. 526–531, 2021, doi: 10.1080/17453674.2021.1932928/SUPPL_FILE/IORT_A_1932928_SM2925.PDF.
[56] S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med Inform Decis Mak, vol. 19, no. 1, pp. 1–16, Dec. 2019, doi: 10.1186/S12911-019-1004-8/FIGURES/12.
[57] J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, “Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review,” Applied Sciences 2020, Vol. 10, Page 1042, vol. 10, no. 3, p. 1042, Feb. 2020, doi: 10.3390/APP10031042.
[58] J. A. M. Sidey-Gibbons and C. J. Sidey-Gibbons, “Machine learning in medicine: a practical introduction,” BMC Medical Research Methodology 2019 19:1, vol. 19, no. 1, pp. 1–18, Mar. 2019, doi: 10.1186/S12874-019-0681-4.
[59] I. Lee and Y. J. Shin, “Machine learning for enterprises: Applications, algorithm selection, and challenges,” Bus Horiz, vol. 63, no. 2, pp. 157–170, Mar. 2020, doi: 10.1016/J.BUSHOR.2019.10.005.
[60] J. Drgoňa et al., “All you need to know about model predictive control for buildings,” Annu Rev Control, vol. 50, pp. 190–232, Jan. 2020, doi: 10.1016/J.ARCONTROL.2020.09.001.