Prediction of Prefecture-Level Subjective Well-Being in Japan by Using Google Trends and Socioeconomic Data: Machine Learning Model Development and Validation Study

JMIR Formative Research 2026 Open access

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Authors: Kenichi KISHI, Hisashi Hayashi, Shigeomi Koshimizu
DOI: 10.2196/88696
Cited by: 0
References: 10

Abstract

Incorporating prespecified Google Trends indicators into leakage-controlled stacked-ensemble models improved a 2025 holdout prediction of subjective well-being by using 2022-2025 data from Japan's 47 prefectures, reducing the mean squared error from 0.0050 to 0.0045.

Fields of Study & Subjects

Psychological Well-being and Life Satisfaction Data-Driven Disease Surveillance Mental Health via Writing

Publication details

Journal / source
JMIR Formative Research
Volume
10
Pages
e88696–e88696
Publication date
6 March 2026
Language
EN