HYBRID TECHNIQUE FOR ENHANCING THE ACCURACY OF EARLY PREDICTION OF CARDIOVASCULAR DISEASE - M, Dineshkumar; D, Sivakumar; S, Jeyabalan (The journal of contemporary issues in business and government, 2021)
Most people actually have heart disease because of work and self depression. Consequently, every year deaths from heart disease are steadily increasing. While new technologies have been developed worldwide, not all of them are successful in diagnosing heart disease well in advance. This paper looked at the early-stage decision-making strategy for heart disease using Decision Tree, k-means, SVM and neural network. This analysis aims primarily to improve the accuracy with hybrid machine learning using the UCI online data repository. Through the results obtained through this research study it is identified that the hybrid machine learning technique DTSVM is provides more accurate result when compared to all other methods. The 88.3 % accuracy is achieved through this hybrid technique and error rate 11.1%.