Effective Prediction of Coronary Heart Disease Using Hybrid Machine Learning

Coronary in the world, coronary heart disease (CHD) is the major cause of death. The number of people diagnosed with heart disease is increasing at an alarming rate, and it is critical and significant to predict these diseases in advance. A major challenge in clinical data research is predicting cardiovascular disease. At this point in time, one person dies from heart disease per minute. As a result, early identification of CHD can aid in the reduction of these rates. When it comes to using traditional methods to anticipate data, the difficulty lies in the data’s complexity and correlations. Machine Learning (ML) algorithms, on the other side, showed tremendous possibilities in outperforming conventional illness diagnosis methods and assisting medical practitioners in the early identification of high disorders. Therefore, this paper demonstrates how effectively predicting coronary heart disease may be done using hybrid machine learning. The prediction concept is explained utilizing various combinations of characteristics and a number of well-known categorization techniques. The suggested study uses a Hybrid Random Forest (RF) with Linear Model, Naive Bayes (NB), and Logistic Regression (LR) classifiers are used to identify the patient’s severity and estimate the probability of coronary heart disease. The prediction approach for CHD that utilizes a combination of Random Forest with Linear model, SVM (Support Vector Machine), and NB can achieve improved performance and accuracy. In this paper, the accuracy, precision, F1-score, and recall of the presented model are utilized to determine its performance.