Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques
Shengkun Xie, Jin Zhang
Abstract
Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance. By implementing these techniques on car accident data, health organizations can identify and forecast a fatal event, enabling more efficient and effective allocation of limited health resources. Concurrently, enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions. Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents, ultimately contributing to improved road safety and better management of health resources. Moreover, the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction. Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.