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Predicting life satisfaction using machine learning and explainable AI.

Alif Elham Khan1, Mohammad Junayed Hasan1, Humayra Anjum1

  • 1Department of Electrical and Computer Engineering, North South University, Plot # 15 Block B, Bashundhara R/A, Dhaka, 1229, Bangladesh.

Heliyon
|May 31, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts life satisfaction using 27 key questions. Large language models also show promise, with health being the top predictor across all age groups.

Keywords:
Ensemble modelExplainable AILife satisfactionMachine learning

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence in Health
  • Psychological Measurement

Background:

  • Traditional methods for measuring life satisfaction are complex and prone to errors.
  • Accurate assessment of life satisfaction is vital for mental health interventions.
  • Existing methods lack validation and widespread applicability.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting life satisfaction.
  • To explore the utility of large language models (LLMs) in life satisfaction prediction.
  • To identify key determinants of life satisfaction across different age groups.

Main Methods:

  • Utilized a Danish government survey dataset of 19,000 individuals (aged 16-64).
  • Applied feature learning to extract 27 significant questions for contentment assessment.
  • Developed ML models and evaluated clinical/biomedical LLMs for prediction, converting tabular data to natural language.

Main Results:

  • ML models achieved 93.80% accuracy and 73.00% macro F1-score.
  • LLMs achieved 93.74% accuracy and 73.21% macro F1-score, with biomedical domain showing stronger correlation.
  • Health condition identified as the most significant determinant of life satisfaction across all ages.

Conclusions:

  • ML and LLMs offer highly accurate and reproducible methods for predicting life satisfaction.
  • Explainable AI (XAI) is crucial for validating and trusting AI-driven insights into well-being.
  • This research provides a robust framework for AI-assisted investigation of subjective well-being.