IDENTIFICATION OF FACTORS INFLUENCING HEART FAILURE MORTALITY USING MACHINE LEARNING METHODS
В В Кукарцев, S.A. ZAMOLOTSKY, V V Khramkov, SibGU named after M.F. Reshetnev
Abstract
Mining is an important industry that plays a crucial role in the extraction of natural resources. However, this work can often be hazardous and pose significant risks to worker health and safety. It is very important for companies to prioritize worker safety and implement the most effective preventative measures to mitigate the effects. To improve working conditions and reduce risks, a study was conducted to identify the factors most influencing fatalities and create a model for prediction. A dataset, of eleven binary, integer and rational attributes and a binary output variable was processed and visualized. An experiment was conducted investigating the predictions using a decision tree, which showed higher accuracy after feature selection. As a result, the most significant influencing factors were identified; also proposed two classifiers that can be used for mortality prediction, which will improve the working conditions in the workplace and increase the efficiency of the mining industry.