Novel Intelligent Model for Heart Disease Prediction using Dynamic KNN (DKNN) with improved accuracy over SVM

Aim: In comparison to Support Vector Machine, the major goal is to forecast the Novel Intelligent model for Heart Disease prediction using Dynamic KNN (SVM). Materials and Procedures: Two machine learning methods, Dynamic KNN (N=92) and Support Vector Machine (N=92), are used to predict heart disease. Dynamic KNN is a simple algorithm used for disease prediction. Heart disease dataset is used for disease prediction. For each group 20 samples are taken and it is divided into training and testing dataset. Result and Discussion: Accuracy of Dynamic KNN is 84.44% and Support Vector Machine is 67.21%. There exists an analytical significant difference between Dynamic KNN and SVM. Conclusion: Dynamic KNN appears to perform significantly better than Support Vector Machine for Novel Heart Disease Prediction.