A Prediction Model for Human Happiness Using Machine Learning Techniques

Chaipornkaew, Piyanuch; Prexawanprasut, Takorn

doi:10.1109/icsitech46713.2019.8987513

Cited by 6 publications

(1 citation statement)

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“…In [12], researchers analyzed the WHI using regression analysis and correlation to study calculation issues related to this index based on seven components of this global indicator for 156 countries collected from the 2016 WHR. The research [13] developed a happiness prediction model using machine learning algorithms such as NB, K-nearest neighbor (KNN), MLP, and Decision Tree (DT), based on survey data collected from employees of the Ministry of Public Health in Thailand. The study employs techniques to address imbalanced data and achieves an overall prediction accuracy of 88.19% using DT.…”

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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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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Statistical Data Mining Methods in Predicting Happiness and Habits

Sulaiman,

Jghef,

Abdullah

et al. 2024

ITM Web Conf.

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The objective of this study is to employ statistical data mining methods and con-duct a survey among young individuals to construct a model capable of forecasting overall happiness. This model will consider over a hundred characteristics, including lifestyle choices and musical tastes. We utilized boosting trees, subset se-lection, and GAM (Generalized Additive Models) techniques. In addition, we created actual test data to validate the model. All available approaches have found many lifestyle variables, including as energy levels, loneliness, desire to alter the past, eating properly, and spending time with friends, as significant determinants of happiness. We generated authentic test data to verify the model, utilizing rigorous testing protocols to evaluate its predicted precision and applicability across various demographics. Based on our investigation, the use of the gradient boost technique resulted in improved picture projections. The evaluation of the technique using a confusion matrix revealed an accuracy of 97.1% for training and a perfect accuracy of 100% for validation. The training phase achieved an accuracy of 62.5%, as shown by the confusion matrix, while the overall confusion matrix demonstrated a 92.0% accuracy in predicting happiness. The support vector machine, trained incrementally, demonstrated encouraging prospects for future investigation.

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