Using Classification to Determine Whether Personality Profiles of Countries Affect Various National Indexes

Yaman, Emine; Kilic, Azra Music; Zerdo, Zaid

doi:10.1109/iccairo.2018.00017

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…This model is based on data collected from 187 countries by the United Nations (UN) Development Project. Based on the Myers-Briggs personality type theory, the study [51] sought to investigate multiple country-level indexes such as the HDI, GDP per capita, and democracy index. To this end, four classification models were proposed using NB, MLP, SVM, and RF.…”

Section: B Machine Learning Models For Qol Predictionmentioning

confidence: 99%

“…These dimensions often vary from one study to another, making it challenging to create standardized datasets that suit all research objectives. Some valuable datasets related to quality of life are provided by international organizations such as the United Nations, World Bank, OECD, and others which are used in these works [63], [81], [50], [72], [73], [51], [52], [53], [54], [64], [75], [74], [8], [69], [76]. These organizations compile and publish data on a wide range of indicators, including income, education, healthcare, and environmental quality.…”

Section: ) Datatypes Sources and Sizesmentioning

confidence: 99%

“…World Happiness index data [73], [8], [74], [76], [69] OECD's Better life index data [81], [72], [52], [53] Human development indicators [50], [52], [76] Eurostat database [63], [72], [54] GDP per capita [51], [75] World Bank data [64] Bloomberg data [53] Corruption Perception Index and Global Peace Index data [74] London bullion market gold prices [40] WHO data [31] Financial news from the website of the EQS Group [53] Regional data in the United States [71] ISO 73120 World Council on City Data [77] Data from The Center for Disease Control's Behavioral Risk Factor Surveillance System and the New York City Department of Health and Mental Hygiene [41] Questionnaire data A proposed survey [43], [65], [35], [91], [45], [62], [69] Survey on College students [32], [47], [48], [61] National Health and Nutrition Examination Survey (NHANES) [44], [49] Survey results of Turkish Statistical Institute [30] Big 5 personality questionnaire [29] The demographic and Oxford Happiness Questionnaires …”

Section: International and Regional Datasetsmentioning

confidence: 99%

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Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

Jannani,

Sael,

Benabbou

2024

IEEE Access

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The concept of "quality of life" (QoL) encompasses all aspects of people's standard of living, including economic, social, or health-related factors, as well as their perceptions of their own lives. Although the growing application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in processing and modeling the diverse datasets associated with these domains, there remains a significant challenge in addressing different issues in QoL area of research and in fully harnessing these technologies to improve QoL research outcomes. Despite the technological advancements, current research endeavors often overlook the complex, multifaceted nature of QoL study. This oversight results in fragmented insights and leaves significant areas underexplored. In this work, we conducted a systematic literature review (SLR) to investigate the contribution of AI to QoL studies. For this, we collected 68 research works published between 2008 and 2022. This review covers a range of research questions about the objectives and methods of studies on QoL, the sources and types of data utilized, and the advancements made through the application of natural language processing (NLP), ML, deep learning (DL), statistical models, and semantic approaches. The goal of this review is to tackle the prevalent ambiguity in QoL dimensions, synthesize the research findings, and highlight the contributions, advancements, and most innovative approaches in the field. Moreover, we identify gaps and limitations in the current literature and suggest potential areas for future research, aiming to inspire more cohesive and comprehensive approaches to studying QoL using AI and ML techniques.

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Section: B Machine Learning Models For Qol Predictionmentioning

confidence: 99%

Section: ) Datatypes Sources and Sizesmentioning

confidence: 99%

Section: International and Regional Datasetsmentioning

confidence: 99%

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Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

Jannani,

Sael,

Benabbou

2024

IEEE Access

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“…Their results demonstrated a negative correlation, implying that happiness must be considered within the context of a particular cultural context. In [11], researchers used the Myers-Briggs personality type theory to analyze numerous national indicators, including the HDI, GDP per capita, and democracy index. Predictive models were built for each of the studied QoL indicators using four classification algorithms: Naive Bayes (NB), Multi-Layer Perceptron (MLP), SVM, and RF, and the best accuracy achieved was 72.8%.…”

Section: Related Workmentioning

confidence: 99%

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

Jannani¹,

Sael²,

Benabbou³

2023

Math. Model. Comput.

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Machine 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.

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Machine Learning Based Personality Classification Using Clustering Algorithm

Sumukha

Reddy

2022

2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)

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Using Classification to Determine Whether Personality Profiles of Countries Affect Various National Indexes

Cited by 3 publications

References 5 publications

Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

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

Machine Learning Based Personality Classification Using Clustering Algorithm

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