ANALISIS FAKTOR RISIKO GAGAL JANTUNG DENGAN REGRESI LOGISTIK BERBASIS IoMT

Abstract

Technology in the era of revolution 4.0, which is currently developing so rapidly, has given birth to Internet of Things technology and can be implemented in the health sector or called the Internet of Medical Things (IoMT). IoMT technology can be applied to monitor heart disease patients and obtain medical record data that is useful for further decision making, such as predicting the potential for heart disease using logistic regression. This study uses medical record data for heart disease with the variable heart failure as the dependent variable and the variables age, gender, diabetes, anemia, hypertension, smoking habits as independent variables. In this research, machine learning was applied with a logistic regression algorithm on clinical data collected via IoMT devices to detect heart disease. Classification. The accuracy of the model was obtained at 75%, so it can be said that the model score is on the average model scale, which means the model is quite good. The average gender of patients who suffer a heart attack is male with an age range of 60-70 years. Furthermore, in patients who have a history of hypertension, a person's risk of developing heart failure increases by 4,2%. Meanwhile, in patients who have a history of diabetes, a person's risk of developing heart failure increases by 4%.