Prediction of Cardiac Arrest Using Support Vector Machine and Rough Set

Cardiac-related problems have taken millions of lives within a small-time interval around the globe. Cardiac arrest does not have any initial symptoms, always a challenge for the physician to overcome this problem. We have developed a novel technique that includes three concepts, i.e., Rough Set Theory (RST), Support Vector Machines (SVM), and Differential Equation Modeling (DEM). Applying RST, we find that Cardiac arrest is the most significant heart-related disease, which leads to a maximum number of deaths in our state Odisha. We have used two fundamental concepts: SVM and DEM, for predicting cardiac arrest accurately. This study is useful for future prediction of cardiac arrest and also helpful in finding the significant heart-related diseases with high mortality rates. The first phase of our analysis deals with raw heart-related medical data applying RST on these data, and we find that cardiac arrest is the most significant heart-related ailment with a high mortality rate. The subsequent section of the paper deals with the symptoms and future prediction of cardiac arrest based on SVM and DME.