USING MACHINE LEARNING TO DEVELOP A RISK PREDICTION MODEL FOR ACUTE MOUNTAIN SICKNESS (Proceedings of the International Conference on Public Health, 2023)
Background: Acute Mountain Sickness (AMS) is a syndrome caused by individuals who are unacclimatized at high altitudes, AMS can threaten health and decrease productivity. By predicting AMS risk, workers can take measures in advance to prevent AMS. The main objective of this study was to use machine learning techniques to develop an AMS risk prediction model. Methods: A retrospective cohort study was conducted to capture AMS monitor data for State Grid workers in the Tibet-Ali project from 1 January 2019 to 31 December 2020. The data was assigned to the training and test sets in 7:3. 10-fold cross-validation was used to improve generalization abilities. Four models including Random Forest (RF) were developed and compared. Area Under the Curve (AUC) and accuracy were used to measure the performance of models. Results: The cohort consisted of 10956 workers, 10438 (95.27%) were male, and the mean age was 36.13 ± 10.49 years. The AMS incidence was 15.58% (n = 1707). The RF model was superior to others in predicting AMS risk. In the test set, the accuracy was 80.32%. After parameter optimization of all models, the RF model still outperformed others, with the best AUC and accuracy were 0.76 and 78.12% ± 7.21%, respectively. Twelve features including demographics, clinical, and altitude were included in the RF model. Conclusions: This study aimed to develop a machine learning-based model for predicting AMS risk among workers at high altitudes. The RF model was found to be the best performer among the four models, based on 12 features known before workers entered plateaus. This model can be an effective tool for estimating AMS risk and guiding decisions regarding AMS primary prevention.
Keywords: Acute Mountain Sickness; machine learning; prediction model; workers