Survival Factors Analysis of Out-of-Hospital Cardiac Arrest Patients via Effective Data Cleaning Techniques and Explainable Machine Learning

The purpose of this study is to explore the key survival factors of OHCA (Out-of-Hospital Cardiac Arrest) non-trauma cases through data science methods and machine learning technology. It is expected to provide directions for improvements in first aid procedures and policy advocacy to increase the survival rate of OHCA cases. This study explores the latest data of OHCA cases in Tainan City, Taiwan. To deal with the issue of data mess in the majority category, a suitable data cleaning method is proposed to ensure the rationality of the cleaned data. In addition, due to the insufficient amount of data and extremely imbalanced data, the oversampling technique is used to generate data in the minority category to balance the dataset. Next, a machine learning model is adopted to predict whether OHCA non-trauma patients eventually survive. Finally, SHAP (SHapley Additive exPlanation) is applied to conduct a comprehensive analysis and interpretation of the model training results to gain new insights into the key survival factors.