Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm

Abstract

Data mining techniques have been widely used to mine knowledgeable information from medical data bases. In data mining classification is a supervised learning that can be used to design models describing important data classes, where class attribute is involved in the construction of the classifier. Nearest neighbor (KNN) is very simple, most popular, highly efficient and effective algorithm for pattern recognition.KNN is a straight forward classifier, where samples are classified based on the class of their nearest neighbor. Medical data bases are high volume in nature. If the data set contains redundant and irrelevant attributes, classification may produce less accurate result. Heart disease is the leading cause of death in INDIA. In Andhra Pradesh heart disease was the leading cause of mortality accounting for 32%of all deaths, a rate as high as Canada (35%) and USA.Hence there is a need to define a decision support system that helps clinicians decide to take precautionary steps. In this paper we propose a new algorithm which combines KNN with genetic algorithm for effective classification. Genetic algorithms perform global search in complex large and multimodal landscapes and provide optimal solution. Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.

Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

“…Akhil Jabbar et al [11] proposed a new algorithm which combines KNN with genetic algorithm for effective classification of heart disease. The author also stated that Genetic algorithms perform global search in complex large and multimodal landscapes and provide optimal solution.…”

Medical Data Classification Using Different Optimization Techniques: A Survey

“…Akhil Jabbar et al [57] have proposed a combined approach of KNN and genetic algorithm for improving classification accuracy of heart disease data set. Proposed system used genetic search to prune redundant and irrelevant attributes also to find attributes which are important for classification.…”

Classification of Poverty Levels Using k-Nearest Neighbor and Learning Vector Quantization Methods

“…Accuracy and precision in the classification of data are very important. In recent years, classification method has been proven in helping many people's work, such as in medical [2], [3], [4], image classification [5], text classification [6] etc. Some methods of classification are often used, such as rulebased, neural networks, support vector machines, naïve Bayes and nearest neighbor.…”

“…The research that has been done before is on classification of heart diseases using k-nearest neighbor and genetic algorithm (GA) [2]. This study provides results that the use of GA in the k-NN method for classification of heart diseases has a better accuracy rate compared with the k-NN method without GA.…”