An Interpretable Predictive Model for Health Aspects of Solvents via Rough Set Theory

This paper presents a machine learning (ML) approach to predict the potential health issues of solvents by uncovering the hidden relationship between substances and toxicity. Solvent selection is a crucial step in industrial processes. However, prolonged exposure to solvents has been found to pose significant risks to human health. To mitigate these hazards, it is crucial to develop a predictive model for health performance by identifying the contributing factors to solvent toxicity. This research aims to develop a predictive model for health issues related to solvent toxicity. Among various algorithms in ML, Rough Set Machine Learning (RSML) was chosen for this work due to its interpretable nature of the generated models. The models have been developed through data collection on the toxicity of various organic solvents, the construction of predictive models with decision rules, and model verification. The results reveal correlations between solvent toxicity and the Balaban index, valence connectivity index, Wiener index, and boiling points. The generated predictive model using RSML has successfully provided insightful observations about the correlation between human toxicity and molecular attributes.