ANALYSIS OF MORTALITY RATE OF HEART FAILURE PATIENTS USING LOGISTIC REGRESSION MODELS - A. Tutuhatunewa, Jacqueline
The heart is one of the organs of the human body that has an important role and the main function of pumping blood to the lungs and throughout the body. Any damage to the heart will cause various complications or diseases in a person's body. Heart disease is a very deadly disease because it has a very high risk of death, such as heart failure. Heart failure is a condition that occurs when the heart fails to pump blood and circulate it throughout the body. A person with heart failure has a higher death rate than other people. To reduce the death rate from heart failure, it is necessary to know what factors affect the death rate of heart failure patients. Therefore, in this study we want to know what factors have a significant effect on the mortality rate of heart failure patients using the logistic regression method. The response variable was patients with heart failure who died. Predictor variables or factors suspected of causing death in heart failure patients were age, anemia, High Blood Pressure, creatinine phosphokinase, diabetes, ejection fraction, gender, platelets, serum creatinine, serum sodium, smoking, and time of treatment. In binary logistic regression modeling, the variables that have the most significant effect on mortality rates in heart failure patients are ejection fraction, serum creatinine, and treatment time, with a model accuracy rate of 81,82%.