A Modified K-Nearest Neighbors Algorithm for the Detection of Heart Disease

Muhammad Muntazir Khan; Shakila Parveen Jan; Jamal Uddin; Basharat Ahmad Hassan; Anees Ur Rahman

Authors

Muhammad Muntazir Khan Institute of Computer Science and Information Technology, ICS/IT, FMCS, the University of Agriculture, Peshawar 25130, Pakistan
Shakila Parveen Jan Department of Information Technology, Qurtuba University, Peshawar, Pakistan
Jamal Uddin Computer Science, Riphah School of Computing and Innovation, Riphah International University, Lahore, Pakistan
Basharat Ahmad Hassan Institute of Computer Science and Information Technology, ICS/IT, FMCS, the University of Agriculture, Peshawar 25130, Pakistan
Anees Ur Rahman Youth affair district youth office upper chitral

Keywords:

Heart Disease, KNN, Jaccard, Cosine, Accuracy, Confusion Matrix

Abstract

The leading cause of mortality worldwide is heart disease, sometimes referred to as cardiovascular disease. It is a dangerous illness that impacts the heart and blood arteries. A significant amount of research and analysis has been done recently with the goal of improving the accuracy and dependability of heart disease data. In this discipline, machine learning is crucial since it provides medical diagnostic tools that may be used to forecast illness and enhance healthcare. In this study, heart disease detection is proposed by combining KNN with Jaccard and Cosine similarities. Further, the results of Jaccard and cosine integrated KNN are compared with the results of state-of-the-art models like KNN and decision trees. Python and its libraries are used for simulation purposes. After the simulation, it was found that Jaccard-based KNN (JKNN) had the best accuracy (97%) according to the study's analysis of the Cleveland heart disease dataset. With 91% accuracy, the Cosine-based KNN (CKNN) likewise demonstrated strong performance. In a similar vein, the decision tree is inadequate for classifying heart disease because of its poor accuracy rate as 85%. Likely, KNN shows average results in the form of accuracy, as 86%. According to the results, the JKNN technique is the best model for this task, closely followed by CKNN. The use of machine learning in the diagnosis and prognosis of heart disease is affected by these discoveries.

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