{'fraction_non_alphanumeric': 0.02621082621082621, 'fraction_numerical': 0.015384615384615385, 'mean_word_length': 4.628205128205129, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Abstract 11823: Machine Learning-Based Model for Prediction of All-Cause Mortality in Low Ejection Function Patients


 Introduction:
 It is important to accurately predict heart failure prognosis for quality of life and selection of the treatment. In this article, we developed the prediction model based on machine learning and show how each variable used in the prediction model contributed to the prediction.
 
 
 Methods:
 We developed the 5-year all-cause mortality prediction model by machine learning. The database contained 1227 cases and 232 variables. We dropped the variables which had over 20% of missing value. We split the database into 80% as training data and 20% as test data. We selected the variables for model development by statistical methods, algorithm-based method, and past findings. The models were developed using selected variables and evaluated the performance with the area under the curve. The best-performing model was analyzed how each variable contributed to the prediction by the SHapley Additive exPlanations (SHAP).
 
 
 Results:
 We selected 19 variables by statistical methods, algorithm-based method, and past findings for the model development. The best model has used six variables, age, creatinine, body surface area, blood urea nitrogen, diabetes mellitus, and emergency hospitalization from all combinations of variables. The AUC was 0.80 in training data and 0.79 in test data. We showed the contributions of variables to the prediction by SHAP in Figure. The variables listed in higher were more affected to the prediction value. The values positioned in more right were contributed to survival.
 
 
 Conclusions:
 We successfully developed the prediction model using six variables and with good AUC.
 
 
 
