Prediction of Cardiac Arrest by the Hybrid Approach of Soft Computing and Machine Learning

Abstract

Cardiac-related diseases are the major reason for the increased mortality rate. The early predictions of cardiac diseases like ventricular fibrillation (VF) are always challenging for doctors and data analysts. Early prediction of these diseases can save million lives. If the symptoms of these diseases are predicted early, the chance of survival increases significantly. For the prediction of Ventricular fibrillation (VF), several researchers have used Heart Rate Variability Analysis (HRV); various alternatives by combining the features taken from several areas to explore the prediction outcome. Several techniques like spectral Analysis, Rough Set Theory (RST), Support Vector Machine (SVM), and Adaboost techniques have not required any pre-processing. In this work, randomly medical-related data sets are taken from various parts of Odisha, applying regression and Rough Set techniques, reducing the dimension of the data set. Application of Rough Set Theory (RST) on the data set is not only useful in dimension reduction but also gives a set of various alternatives. This work's last section uses a comparative analysis between AdaBoost combined with RST and Empirical mode decomposition (EMD).