Predictive data analysis to identify heart anomalies

Abstract

Heart disease is a usually used word to describe diseases related to heart, when heart is not efficiently performing at is best, most of this disease is acquired because of unhealthy lifestyle and unhealthy food. Heart diseases need regular care to improve the patient’s quality of life. We can analyze cardiac disabilities of a individual by factors like historical health data and risk factors. The fusion of algorithms with clinical data can forecast the results of any disease so, incorporating these two things for Predicting and diagnosis of the heart functionality using the computational algorithms where the user interface in developed in R studio. Foremost objective of the system is for majorly predicting the heart anomalies collected using the real time clinical data. The proposed method uses the performance comparison of the algorithms and as well as the datasets like random forest and logistic regression to calculate which gives highest accuracy rate performance and this study also involves use of two different datasets, one which is available in the existing dataset for heart disease and another which was collected from the hospital in real time, so this can help in making an efficient system that can be utilized to predict the probability of heart diseases of any individual. Thus this can form a foundation for any therapy or treatment to be given this would increase the efficiency as well as help the medical staff and doctors to predict heart disease and more accurately. Computer diagnosis and prediction of a disease can solve many medical problems by predicting it beforehand.