Measuring sadness index based on country statistics
Artur Samojluk
University of Warmia and Mazury in OlsztynBartosz Nowak
University of Warmia and Mazury in OlsztynKarolina Papiernik
Abstract
The article studied topics related to measuring people’s sadness. For this purpose, the question was asked which factor: social, economic or climate, matters most. The paper analyzed, using machine learning, statistical data related to the number of suicides against the factors: level of Internet access, average income, temperature in a country and, in addition, population density. The method used was correlational statistical analysis using the K-nearest neighbor (KNN) method and also Pearson’s correlation. The results were visualized in the form of graphs, then subjected to final analysis and included in the form of final conclusions.
Keywords:
happiness index, sadness index, k-nn, regression, machine learningReferences
BEYER K., GOLDSTEIN J., RAMAKRISHNAN R., SHAFT U. 1999. When Is “Nearest Neighbor Meaningful?”. Lecture Notes in Computer Science, 1540.
Crossref
Google Scholar
BOGOMOLOV A., LEPRI B., PIANESI F. 2013. Happiness Recognition from Mobile Phone Data. International Conference on Social Computing, 08-14 September 2013, Alexandria, VA, USA. https://doi.org/10.1109/SocialCom.2013.118.
Crossref
Google Scholar
Business and economic data for 200 countries. 2022. The Global Economy. https://www.theglobaleconomy.com/rankings/gdp_per_capita_current_dollars/. Google Scholar
COVER T., HART P. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1): 21–27.
Crossref
Google Scholar
CPALKA K., NOWICKI R.K., RUTKOWSKI L. 2007. Rough-Neuro-Fuzzy Systems for Classifaction. The First IEEE Symposium on Foundations of Computational Intelligence (FOCI’07).
Crossref
Google Scholar
DHARANEESHWARAN, NITHYA S., SRINIVASAN A., SENTHILKUMAR M. 2017. Calculating the user-item similarity using Pearson’s and cosine correlation. International Conference on Trends in Electronics and Informatics (ICEI), p. 1000-1004. https://doi.org/10.1109/ICOEI.2017.8300858.
Crossref
Google Scholar
DREHMER J.E. 2018. Sex differences in the association between countries’ smoking prevalence and happiness ratings. Public Health, 160: 41-48. https://doi.org/10.1016/j.puhe.2018.03.027.
Crossref
Google Scholar
IBNAT F., GYALMO J., ALOM Z., AWAL M.A., AZIM M.A. 2021. Understanding World Happiness using Machine Learning Techniques. International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2).
Crossref
Google Scholar
IVANOVÁ M., KLAMÁR R., FECKOVÁ-ŠKRABUĽÁKOVÁ E. 2022. Identification of factors influencing the quality of life in European Union countries evaluated by Principal Component Analysis. Geographica Pannonica, 26: 13-29. https://doi.org/10.5937/gp26-34191.
Crossref
Google Scholar
KAUR M., DHALARIA M., SHARMA P.K., PARK J.H. 2019. Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring. Applied Sciences, 9: 1613. https://doi.org/10.3390/app9081613.
Crossref
Google Scholar
KUMAR T. 2015. Solution of Linear and Non Linear Regression Problem by K Nearest Neighbour Approach: By Using Three Sigma Rule. IEEE International Conference on Computational Intelligence & Communication Technology, p. 197-201. https://doi.org/10.1109/CICT.2015.110.
Crossref
Google Scholar
List of Countries by Average Temperature. 2022. List First. https://listfist.com/list-of- countries-by-average-temperature. Google Scholar
List of countries by suicide rate. 2022. Wikipedia. https://en.wikipedia.org/wiki/List_of_countries_by_suicide_rate. Google Scholar
MA W., TAN K., DU Q., DING J., YAN Q. 2016. Estimating soil heavy metal concentration using hyperspectral data and weighted K-NN method. 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). https://doi.org/10.1109/WHISPERS.2016.8071813.
Crossref
Google Scholar
MARVIN H., GRUBER J. 1990. Regression Estimators. A Comparative Study. Academic Press, Stanford University, California. Google Scholar
NOWICKI R.K., NOWAK B.A., WOZNIAK M. 2014. Rough k nearest neighbours for classification in the case of missing input data. In: Proceedings of the 9th International Conference on Knowledge, Information and Creativity Support Systems, Limassol, Cyprus, November 6-8, 2014. Ed. G.A. Papadopoulos. University of Cyprus, Nicosia, Cyprus. Google Scholar
NOWICKI R.K., NOWAK B.A., STARCZEWSKI J.T., CPAŁKA K. 2014. The Learning of Neuro-Fuzzy Approximator with Fuzzy Rough Sets in Case of Missing Features. International Joint Conference on Neural Networks, p. 3759-3766.
Crossref
Google Scholar
POLKOWSKI L., ARTIEMJEW P. 2015. Granular Computing in Decision Approximation. An Application of Rough Mereology. Series: Intelligent Systems Reference Library, 77. https://doi.org/10.1007/978-3-319-12880-1_1.
Crossref
Google Scholar
POLKOWSKI L.T. 2022. What Logics for Computer and Data Sciences, and Artificial Intelligence. Studies in Computational Intelligence (SCI), p. 992.
Crossref
Google Scholar
QI X., GAO Y., LI Y., LI M. 2022. K-nearest Neighbors Regressor for Traffic Prediction of Rental Bikes. 14th International Conference on Computer Research and Development (ICCRD), p. 152-156, https://doi.org/10.1109/ICCRD54409.2022.9730527.
Crossref
Google Scholar
RAJNOHA R., LESNÍKOVÁ P., VAHANČÍK J. 2021. Sustainable economic development: The relation between economic growth and quality of life in V4 and Austria. Economics and Sociology, 14(3): 341-357. https://doi.org/10.14254/2071-789X.2021/14-3/18.
Crossref
Google Scholar
SKIDELSKY E. 2014. What Can We Learn From Happiness Surveys? Journal of Practical Ethics, 2(2): 20-32. Google Scholar
University of Warmia and Mazury in Olsztyn
University of Warmia and Mazury in Olsztyn