Disease Prediction in Victims of Chemical Exposure Using Forced Expiratory Volume; Logistic Regression vs. Machine Learning Methods

Predicting the presence of respiratory diseases at minimal cost in individuals exposed to chemical weapons, is an ideal goal. This study employed Machine Learning and Logistic Regression methods to develop and compare predictive models for determining the health status of individuals and eventually compare the efficiency of these models’ performance.

This cross-sectional analytical study was conducted on 320 male individuals in Sardasht City who were affected by the harmful effects of Mustard gas (a sulfur-based chemical warfare weapon) in 2023. Predictors such as age, gender, and FEV₁ at the first measurement and again two years later were used. This article has measured FEV₁ values of 2005 and 2007. Using SPSS 22 software, the researcher utilized a modified version of the Logistic regression formula that calculates the probability of occurrence, tailored to meet the specific needs of the problem.

The mean FEV₁ value at the first measurement (2005) was 73.10±20.70, and at the second measurement (2007) was 82.10±21.81. Calculated predictive accuracy for the ML 0.9, ML 0.8, ML 0.7, ML 0.6, ML 0.5 Logistic Regression models were 0.813, 0.809, 0.806, 0.813, 0.813, and 0.806, respectively. The agreement between the logistic regression models and ML 0.9, ML 0.8, ML 0.7, ML 0.6, ML 0.5 were 98.8%, 98.8%, 98.4%, 98.4%, and 99.6%, respectively. No significant differences were observed in the performance of Machine Learning and Logistic Regression models on data from chemical warfare casualties.

Using the FEV1 is recommended for the early detection of pulmonary diseases in individuals with respiratory disorders.