Tree-Based Algorithms and Association Rule Mining for Predicting Patients’ Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic: Application of Data Mining

Abstract

Wei-Chun Lin,1– 3 Chien-Hsiung Huang,2– 4 Liang-Tien Chien,4,5 Hsiao-Jung Tseng,6,7 Chip-Jin Ng,2,3,8 Kuang-Hung Hsu,2,3,9– 12 Chi-Chun Lin,2,3,13 Cheng-Yu Chien2– 4,13,14 1Department of Emergency Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, New Taipei City, Taiwan; 2Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan; 3Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; 4Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan; 5Fire Department, Taoyuan City Government, Taoyuan, Taiwan; 6Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan; 7Biostatistics Unit, Clinical Trial Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; 8Department of Emergency Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan; 9Laboratory for Epidemiology, Chang Gung University, Taoyuan, Taiwan; 10Laboratory for Epidemiology, Department of Health Care Management, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan; 11Research Center for Food and Cosmetic Safety, College of Human Ecology, Chang Gung University of Science and Technology, Taoyuan, Taiwan; 12Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan; 13Department of Emergency Medicine, Ton-Yen General Hospital, Zhubei, Taiwan; 14Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, TaiwanCorrespondence: Cheng-Yu Chien, Department of Emergency Medicine, Chang Gung Memorial Hospital, No. 5 Fushing St., Gueishan Dist, Taoyuan City, Taiwan, Tel [phone] # 2505, Fax [phone], Email [email protected]: The authors performed several tree-based algorithms and an association rules mining as data mining tools to find useful determinants for neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients as well as to assess the effect of the first-aid and basic characteristics in the EMS system.Patients and Methods: This was a retrospective cohort study. The outcome was Cerebral Performance Categories grading on OHCA patients at hospital discharge. Decision tree-based models inclusive of C4.5 algorithm, classification and regression tree and random forest were built to determine an OHCA patient’s prognosis. Association rules mining was another data mining method which we used to find the combination of prognostic factors linked to the outcome.Results: The total of 3520 patients were included in the final analysis. The mean age was 67.53 (± 18.4) year-old and 63.4% were men. To overcome the imbalance outcome issue in machine learning, the random forest has a better predictive ability for OHCA patients in overall accuracy (91.19%), weighted precision (88.76%), weighted recall (91.20%) and F1 score (0.9) by oversampling adjustment. Under association rules mining, patients who had any witness on the spot when encountering OHCA or who had ever ROSC during first-aid would be highly correlated with good CPC prognosis.Conclusion: The random forest has a better predictive ability for OHCA patients. This paper provides a role model applying several machine learning algorithms to the first-aid clinical assessment that will be promising combining with Artificial Intelligence for applying to emergency medical services.Keywords: cardiac arrest, tree-based algorithms, data mining

Keywords

• cardiac arrest
• tree-based algorithms
• data mining