@inproceedings{64e921cd75dd458b925beba7023e9dab,
title = "Exploring the Determinants of Well-Being: Insights from SHAP and ICE Analyses",
abstract = "This study applies Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI) techniques to identify important factors influencing individual well-being. Using data from the “Human Information Database” and a well-being survey, a CatBoost regression model was selected as the best model. SHapley Additive Explanations (SHAP) and Individual Conditional Expectation (ICE) were then used to analyze the impact of various factors on well-being, both overall and at a type-specific level. Additionally, SHAP-based clustering was performed to group individuals with similar well-being drivers. A marketing-focused individual-level analysis was also conducted. These results provide actionable insights for developing personalized strategies to enhance well-being, offering practical applications through data-driven analysis.",
keywords = "AutoML, Consumer Well-being, ICE, Marketing, SHAP",
author = "Yu Zhao and Michiko Tsubaki",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 11th International Conference on Information Management, ICIM 2025 ; Conference date: 28-03-2025 Through 30-03-2025",
year = "2026",
doi = "10.1007/978-3-031-99353-4\_34",
language = "English",
isbn = "9783031993527",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "398--414",
editor = "Shuliang Li",
booktitle = "Information Management - 11th International Conference, ICIM 2025, Revised Selected Papers",
}