Categorizing the Heart Syndrome Condition by Predictive Analysis Using Machine Learning Approach
R. Krishnamoorthy, B. S. Liya, S. Arun, S Padmapriya, B Gunasundari, R Thiagarajan
Abstract
As the age of the elderly patients are getting increased, the health factors are also getting affected. By the drastic increase of the human being population, the health factor has to be checked and monitored accordingly. One of the main factors which have to be monitored is the heart rate. Heart rate is said to be the rate of which the heart beat per minute. Heart arrhythmia/ irregular heartbeat can cause heart stroke which causes fatal too. Heart stroke occurs based upon the vibratory state of the heartbeat. If the Heartbeat is mild when compared to the normal heart beat, then the pulse rate is low for the patient. If the Heartbeat is irregular which can vibrate in high stroke then it is strong heart stroke/ severe cardiac arrest where the blood flow is excessive to the artery. Based upon the heart stroke, the range of cardiac arrest can be detected. Blood pressure is also maintained identify the flow of excessive flow. By detecting both the heart rate and blood pressure state, the stroke can be detected. In this novel paper, describe the way of identifying different variations in the heartbeat and blood pressure using machine learning approach. Supervised learning classifies the data in comparative analysis by using SVM, random regression and other techniques. These compares the statistical data of the previous data report by classifying and comparing the data into a statistical report via application.