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

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.