An Enhanced Multi Layer Neural Network to Detect Early Cardiac Arrests
Ranga Swamy Sirisati, C. Srinivasa Kumar, A. Gautami Latha, B. Narendra Kumar, Kanusu Srinivas a Rao
Abstract
Patients and doctors are paying increasing attention to health-care automation because it can save a person's life by predicting ailments early. Many people are suffering from chronological diseases as a result of altering eating habits, regardless of age or gender. “Heart Attacks” is a severe ailment that requires attention from time to time. To date, all automated systems have built models using either classical or ensemble machine learning techniques. Overfitting has affected only a few of these systems, such as random forest and SVM algorithms. As a result, the proposed approach has chosen the “Multi Layer Preceptron” neural network technique, which solves the problem of overfitting and generates an accurate number of correct labels linked with the training model. Instead of using all of the variables mentioned in the dataset, the suggested method assists clinicians in predicting a heart attack in a user at an early stage by assessing only 7 top informative attributes. The model was also compared to other classifiers in order to establish the state of the art, which was determined to be “97.23 percent.”