Work

Experimental Verification of Performance Improvement in Heat-Related Illness Prediction Using Clinical Significance-Based Binning

Year: 2025

Type: article

Abstract: Due to climate change, the frequency and intensity of heat waves are increasing, leading to a rise in heat-related illnesses, particularly affecting outdoor workers. Existing studies have developed predictive models using hospital clinical data and measurable data from wearable devices, but they face limitations in prediction accuracy due to insufficient training data and overfitting. This study proposes a clinical significance-based binning method to address these issues in predicting heat illness occurrence. Through experiments utilizing various machine learning algorithms and comparisons with other binning methods, including data-driven binning methods, the effectiveness of this binning approach was verified. Additionally, by conducting comparative validation using datasets composed solely of features measurable by wearable devices such as smartwatches, this study demonstrates that the proposed binning method can also be applied to real-time heat illness prediction systems for outdoor workers.

Source: Applied Sciences

DOI: 10.3390/app152312500

Authors: Jeongwoo Shin, Hanjo Jeong

Institution: Mokpo National University

Language: English

Topic: Thermoregulation and physiological responses

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896