Performance-enhanced KNN algorithm-based heart disease prediction with the help of optimum parameters

Abstract

Heart diseases are diseases with a high mortality rate. Clinical methods and machine learning methods have been used frequently in the diagnosis of the disease. In this study, the KNN algorithm was used for diagnosis. In order to increase the performance of the algorithm, parameter adjustment has been made and in this context; Manhattan, Euclidean and Chebyshev distance measurements, Uniform and Distance weighting methods and neighbor numbers between 1…15 have been tested on UCI Statlog and Cleveland datasets. The highest classification accuracy for the Statlog dataset is 67.90%, which is obtained with the number of neighbors = 5, the distance method = Euclidian, and the weighting = Distance. Genetic algorithms were also run on the same data set and 88.88% accuracy was obtained for the number of neighbors = 5, distance method = Euclidean and weighting = Distance. While the maximum classification accuracy obtained for the Cleveland dataset was 71.42% before optimization, it was measured as 90.11% after optimization. The parameters that give the highest classification accuracy for the Cleveland dataset are; number of neighbors = 3, distance method = Manhattan and weighting = Uniform.