A Collaborative Prediction of Presence of Arrhythmia in Human Heart with Electrocardiogram Data using Machine Learning Algorithms with Analytics

Abstract

Human heart is the major organ of human being which could fail the other systems in the body at the same time. Hence predicting heart disease is one of the challenging researches that requires meticulous analysis of heart rhythms properly. The irregular heart rhythms or beat is referred to as the Arrhythmia where heart rhythms with low or high rates comparing to the normal heart beat rate which ranges from 60 to 100 beats per minute. The heartbeat can be monitored and identified with the electrical disorder disease called Arrhythmia. This is very deadly when untreated for a long time as mortality rate is extremely high. Hence a prediction system is required to identify the irregular nature of heart and predict the heart problem in the future. The major objective of this research paper is to predict the presence of arrhythmia which is caused as a result of electrical imbalance and irregular heart beat in human being. The prediction is formulated with the help of essential parameters from electrocardiogram like age, gender, height, weight, BMI, QRS duration, P-R interval, Q-T interval, T interval, P interval, QRS, T, P, QRST, J values which will help the prediction of Arrhythmia in human to the best. The dataset sample is collected from UCI Repository based on electrocardiogram report values and pre-processed using Mat lab. The data is converted into test data and prediction is expected to be completed using Machine Deep learning Algorithms as they could be the best models for disease or syndrome predictions. Finally, the Analytics is carried out using Rapid Miner Studio where machine learning algorithms is applied and results obtained. The research will be a starter for futuristic research on automatic prediction of heart disease in human beings with various other parameters.