Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators

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dc.citation.epage546
dc.citation.issue2
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage534
dc.contributor.affiliationУніверситет Хасана ІІ Касабланки
dc.contributor.affiliationHassan II of Casablanca University
dc.contributor.authorДжанані, А.
dc.contributor.authorСаель, Н.
dc.contributor.authorБенаббу, Ф.
dc.contributor.authorJannani, A.
dc.contributor.authorSael, N.
dc.contributor.authorBenabbou, F.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-04T10:28:12Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractАлгоритми машинного навчання відіграють важливу роль в аналізі складних даних у дослідженнях у різних сферах. У цій статті використовуються алгоритми множинної регресії та статистичні методи для дослідження зв’язку між об’єктивними та суб’єктивними показниками якості життя та виявлення ключових факторів, що впливають на щастя на міжнародному рівні на основі даних індексу людського розвитку та Світового індексу щастя, що охоплюють період з 2015 по 2021 рік. Кореляційний аналіз Пірсона показав, що щастя пов’язане з показником індексу людського розвитку та валовим національним доходом на душу населення. Найефективнішою моделлю для прогнозування щастя була випадкова лісова регресія з показником R20.93667, середньоквадратичною помилкою 0.0033048 і середньоквадратичним значенням 0.05748, за якою йшли регресія XGBoost і регресія дерева рішень відповідно. Ці моделі показали, що валовий національний дохід на душу населення є найважливішою характеристикою для прогнозування щастя.
dc.description.abstractMachine learning algorithms play an important role in analyzing complex data in research across various fields. In this paper, we employ multiple regression algorithms and statistical techniques to investigate the relationship between objective and subjective quality of life indicators and reveal the key factors affecting happiness at the international level based on data from the Human Development Index and the World Happiness Index covering the period from 2015 to 2021. The Pearson correlation analysis showed that happiness is related to the HDI score and GNI per capita. The best-performing model for forecasting happiness was the random forest regression, with a R2 score of 0.93667, a mean squared error of 0.0033048, and a root mean squared error of 0.05748, followed by the XGBoost regression and the Decision Tree regression, respectively. These models indicated that GNI per capita is the most significant feature in predicting happiness.
dc.format.extent534-546
dc.format.pages13
dc.identifier.citationJannani A. Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators / A. Jannani, N. Sael, F. Benabbou // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 534–546.
dc.identifier.citationenJannani A. Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators / A. Jannani, N. Sael, F. Benabbou // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 534–546.
dc.identifier.doidoi.org/10.23939/mmc2023.02.534
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/63415
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 2 (10), 2023
dc.relation.ispartofMathematical Modeling and Computing, 2 (10), 2023
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dc.relation.referencesen[1] Radford J., Joseph K. Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science. Frontiers in Big Data. 3, 18 (2020).
dc.relation.referencesen[2] Grimmer J., Roberts M. E., Stewart B. M. Machine Learning for Social Science: An Agnostic Approach. Annual Review of Political Science. 24 (1), 395–419 (2021).
dc.relation.referencesen[3] WHOQOL – Measuring Quality of Life| The World Health Organization. https://www.who.int/tools/whoqol.
dc.relation.referencesen[4] Davis E., Waters E., Shelly A., Gold L. Children and Adolescents, Measuring the Quality of Life of. International Encyclopedia of Public Health. 641–648 (2008).
dc.relation.referencesen[5] Helliwell J. F., Layard R., Sachs J. D., Neve J.-E. D., Aknin L. B., Wang S. World Happiness Report (2022). https://worldhappiness.report/ed/2022/.
dc.relation.referencesen[6] Human Development Index, United Nations. https://hdr.undp.org/data-center/human-development-index.
dc.relation.referencesen[7] Taner M., Sezen B., Mihci H. An Alternative Human Development Index Considering Unemployment. South East European Journal of Economics and Business. 6 (1), 45–60 (2011).
dc.relation.referencesen[8] Martinez R. Inequality and the new human development index. Applied Economics Letters. 19 (6), 533–535 (2012).
dc.relation.referencesen[9] Saputri T. R. D., Lee S. D. A Study of Cross-National Differences in Happiness Factors Using Machine Learning Approach. International Journal of Software Engineering and Knowledge Engineering. 25 (09n10), 1699–1702 (2015).
dc.relation.referencesen[10] Basu R., Behera S. K., Adak D. K. Human Development and Happiness: Are the Two Interlinked? International Journal of Indian Psychology. 6 (3), 141–150 (2018).
dc.relation.referencesen[11] Yaman E., Music-Kilic A., Zerdo Z. Using Classification to Determine Whether Personality Profiles of Countries Affect Various National Indexes. 2018 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). 48–52 (2018).
dc.relation.referencesen[12] Carlsen L. Happiness as a sustainability factor. The world happiness index: a posetic-based data analysis. Sustainability Science. 13 (2), 549–571 (2018).
dc.relation.referencesen[13] Chaipornkaew P., Prexawanprasut T. A Prediction Model for Human Happiness Using Machine Learning Techniques. 2019 5th International Conference on Science in Information Technology (ICSITech). 33–37 (2019).
dc.relation.referencesen[14] Riyantoko P. A. Southeast Asia Happiness Report in 2020 Using Exploratory Data Analysis. International Journal of Computer, Network Security and Information System. 2 (1), 1 (2020).
dc.relation.referencesen[15] Dixit S., Chaudhary M., Sahni N. Network Learning Approaches to study World Happiness. ArXiv:2007.09181 (2020).
dc.relation.referencesen[16] Okagbue H. I., Oguntunde P. E., Bishop S. A., Adamu P. I., Akhmetshin E. M., Iroham C. O. Significant Predictors of Henley Passport Index. Journal of International Migration and Integration. 22 (1), 21–32 (2021).
dc.relation.referencesen[17] Jannani A., Sael N., Benabbou A. Predicting Quality of Life using Machine Learning: case of World Happiness Index. 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). 1–6 (2021).
dc.relation.referencesen[18] Pawliczek A., Kurowska-Pysz J., Smilnak R. Relation between Globe Latitude and the Quality of Life: Insights for Public Policy Management. Sustainability. 14 (3), 1461 (2022).
dc.relation.referencesen[19] Farooq S. A., Shanmugam S. K. A Performance Analysis of Supervised Machine Learning Techniques for COVID-19 and Happiness Report Dataset. Sentimental Analysis and Deep Learning. 591–601 (2022).
dc.relation.referencesen[20] Khder M. A., Sayf M., Fujo S. W. Analysis of World Happiness Report Dataset Using Machine Learning Approaches. International Journal of Advances in Soft Computing and its Applications. 14 (1), 15–34 (2022).
dc.relation.referencesen[21] Home. Human Development Reports. https://hdr.undp.org/.
dc.relation.referencesen[22] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 12 (85), 2825–2830 (2011).
dc.relation.referencesen[23] sklearn.preprocessing.StandardScaler scikit-learn. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html.
dc.relation.referencesen[24] Helliwell J. F., Huang H., Wang S., Norton M. World Happiness, Trust and Deaths under COVID-19 (2021).
dc.relation.referencesen[25] Nettleton D. Chapter 6 – Selection of Variables and Factor Derivation. Commercial Data Mining. Processing, Analysis and Modeling for Predictive Analytics Projects. 79–104 (2014).
dc.relation.referencesen[26] Jolliffe I. T., Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 374 (2065), 20150202 (2016).
dc.relation.referencesen[27] Angelini C. Regression Analysis. Reference Module in Life Sciences. Encyclopedia of Bioinformatics and Computational Biology. 1, 722–730 (2019).
dc.relation.referencesen[28] Shobha G., Rangaswamy S. Chapter 8 – Machine Learning. Handbook of Statistics. 38, 197–228 (2018).
dc.relation.referencesen[29] Misra S., Li H., He J. Chapter 5 – Robust geomechanical characterization by analyzing the performance of shallow-learning regression methods using unsupervised clustering methods. Machine Learning for Subsurface Characterization. 129–155 (2020).
dc.relation.referencesen[30] Fathi E., Shoja B. M. Chapter 9 – Deep Neural Networks for Natural Language Processing. Handbook of Statistics. 38, 229–316 (2018).
dc.relation.referencesen[31] Simske S. Chapter 4 – Meta-analytic design patterns. Meta-Analytics. 147–185 (2019).
dc.relation.referencesen[32] Banks D. L., Fienberg S. E. Statistics, Multivariate. Encyclopedia of Physical Science and Technology (Third Edition). 851–889 (2003).
dc.relation.referencesen[33] Basak D., Pal S., Patranabis D. Support Vector Regression. Neural Information Processing – Letters and Reviews. 11 (10), 203–224 (2007).
dc.relation.referencesen[34] Dong J., Chen Y., Yao B., Zhang X., Zeng N. A neural network boosting regression model based on XGBoost. Applied Soft Computing. 125, 109067 (2022).
dc.relation.referencesen[35] Torgo L. Regression Trees. Encyclopedia of Machine Learning and Data Mining. 1080–1083 (2017).
dc.relation.referencesen[36] Williams B., Halloin C., L¨obel W., Finklea F., Lipke E., Zweigerdt R., Cremaschi S. Data-Driven Model Development for Cardiomyocyte Production Experimental Failure Prediction. Computer Aided Chemical Engineering. 48, 1639–1644 (2020).
dc.relation.referencesen[37] Abirami S., Chitra P. Chapter Fourteen – Energy-efficient edge based real-time healthcare support system. Advances in Computers. 117 (1), 339–368 (2020).
dc.relation.referencesen[38] Chicco D., Warrens M. J., Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 7, e623 (2021).
dc.relation.referencesen[39] Benard C., Da Veiga S., Scornet E. Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA. Biometrika. 109 (4), 881–900 (2022).
dc.relation.referencesen[40] Scornet E. Trees, forests, and impurity-based variable importance. ArXiv:2001.04295 (2020).
dc.relation.referencesen[41] Shi X., Wong Y. D., Li M. Z.-F., Palanisamy C., Chai C. A feature learning approach based on XGBoost for driving assessment and risk prediction. Accident Analysis & Prevention. 129, 170–179 (2019).
dc.relation.referencesen[42] 4.2. Permutation feature importance. https://scikit-learn/stable/modules/permutation_importance.html.
dc.relation.urihttps://www.who.int/tools/whoqol
dc.relation.urihttps://worldhappiness.report/ed/2022/
dc.relation.urihttps://hdr.undp.org/data-center/human-development-index
dc.relation.urihttps://hdr.undp.org/
dc.relation.urihttps://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
dc.relation.urihttps://scikit-learn/stable/modules/permutation_importance.html
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectмашинне навчання
dc.subjectпоказники якості життя
dc.subjectрегресія випадкового лісу
dc.subjectрегресія XGBoost
dc.subjectрегресія дерева рішень
dc.subjectстатистичний аналіз
dc.subjectmachine learning
dc.subjectquality of life indicators
dc.subjectRandom Forest Regression
dc.subjectXGBoost Regression
dc.subjectDecision Tree Regression
dc.subjectstatistical analysis
dc.titleMachine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators
dc.title.alternativeМашинне навчання для аналізу якості життя за допомогою світового індексу щастя та індикаторів людського розвитку
dc.typeArticle

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Mathematical Modeling And Computing. – 2023. – Vol. 10, No. 2