Heart Disease Prediction Using Modified Machine Learning Algorithm

Heart patient number is escalating day by day, and numerous individuals lost their precious lives each year due to sudden heart attack in all over the world. Because of this, before time diagnosis of cardiovascular disease is necessary to prevent death. Some technology-based software is required to help in medical field to recognize heart patients with more accuracy and lesser time. Huge amount of heart patients are present in different hospitals in all over the world, which can be used efficiently to diagnose the heart disease by applying data mining techniques. In the process of data mining, knowledge or useful information is extracted among the large sets of raw data. In the prediction analysis, machine learning techniques are applied to discover valuable patterns and forecast future events or trends. This research work will predict the likelihood of coronary heart disorder in patients by implementing a modified machine learning algorithm. The input data are passed through various procedures comprising preprocessing, clustering, and selection of effective attributes before classification. To determine the heart illness, four algorithms which include random forest, K-means, genetic algorithm, and logistic regression are assimilated. In this technique, the irrelevant attributes of heart dataset are discarded to improve the performance and to decrease the training period time. This process is completed by random forest technique. K-means clusters are optimized by genetic algorithm in order to group all the outlier data points. At last, logistic regression is applied to classify the patients based on the heart disease. Performance comparison among various existing techniques has analyzed on the basis of some performance measures. The calculated accuracy increased up to 95%.