Work

Machine Learning Prediction of Quality of Life: Insight from Property Crime and Tropical Climate Analysis

Year: 2024

Type: article

Abstract: This study delves into predicting the quality of life, a widely researched concept spanning various domains, employing mixed methodologies and indicators. Common predictors of overall quality of life encompass health status, socioeconomic factors, subjective well-being, and environmental conditions. Consequently, this research investigates the potential of utilizing machine learning prediction models for quality-of-life prediction, focusing on two key indicators.Five machine learning algorithms—Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Machine—are empirically compared. Their performance in predicting quality of life is assessed based on property crime and tropical climate (temperature) attributes. Despite an initially low correlation value, temperature yields valuable insights for specific algorithms, enhancing predictive accuracy. This underscores the intricate and nuanced impact of seemingly unrelated factors in machine learning. The Support Vector Machine emerges as the top-performing algorithm, followed by Random Forest and Decision Tree. This paper offers a foundational research framework to aid educators and researchers in exploring quality of life prediction in-depth, utilizing property crime and temperature as key factors.

Source: IAES International Journal of Artificial Intelligence

DOI: 10.11591/ijai.v13.i4.pp4509-4515

Authors: SITI RASIDAH MD SAKIP, Anis Zulaikha Mohd Zukri, Suraya Masrom

Institutions: Universiti Teknologi MARA System, Universiti Teknologi MARA

Language: English

Topic: Quality of Life Measurement

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: diamond

Funder: Universiti Teknologi MARA

Award: G854476752