Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts

This study develops and validates prediction models for heatstroke cases (all cases, hospital admissions, and deaths) per city per 12 hours using weather data and a population-based database from 16 Japanese cities. Machine learning models, incorporating multiple weather variables and techniques like under-sampling and bagging, significantly improved prediction accuracy compared to models using only wet bulb globe temperature (WBGT). The optimal models achieved mean absolute percentage errors (MAPEs) of 14.8% for all heatstroke cases and 10.6% for hospital admissions and deaths, demonstrating sufficient accuracy for public health applications.

Soshiro Ogata, Misa Takegami, Taira Ozaki, Takahiro Nakashima, Daisuke Onozuka, Shunsuke Murata, Yuriko Nakaoku, Koyu Suzuki, Akihito Hagihara, Teruo Noguchi, Koji Iihara, Keiichi Kitazume, Tohru Morioka, Shin Yamazaki, Takahiro Yoshida, Yoshiki Yamagata, Kunihiro Nishimura