International Journal of Advanced Engineering Research and Science (IJAERS)
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
Heart Disease Prediction using K Nearest Neighbour and K Means Clustering Dr.Mohanraj.E1,SubhaSuryaa.K2, Sudha.P3, Sarath Kumar.K4
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Assistant Professor, Department of CSE, K.S.Rangasamy College of Technology, Tiruchengode,Tamil Nadu, India 2, 3, 4 Students, Department of CSE, K.S.Rangasamy College of Technology, Tiruchengode,Tamil Nadu, India
Abstract—The widespread application of data mining is highly noticeable fields like e-business, marketing and retail has led to its application in other industries and healthcare sectors. The healthcare environs are still information rich but that has poor knowledgeable data. Techniques in Data mining have been commonly used to extract knowledgeable information from medical data bases Today medical field have come a long way to treat patients with various kind of diseases. Among the most menacing one is the Heart disease which cannot be detected with a stripped eye and comes suddenly when its boundaries are reached. Bad medical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment computer-based and support Systems can be developed to make good decision. Many hospitals use hospital information systems to manage their healthcare or patient data. These systems produce huge amounts of data in the form of images, text, charts and numbers. K nearest neighbor and K means used to support the medical decision making efficiently. Keywords—K Nearest Neighbor, K Means. I. INTRODUCTION Heart disease is one of the major problems for causing death. Most of the healthcare organization predicts this disease by doctor’s experience. Nowadays our computer technology has been improved and develops software for analyzing the problems in our human body. The large amount of healthcare data can be collected by health care industry for every person, those details does not contain hidden information. In this case advanced data mining techniques are used to evaluate the dataset effectively, which helps as to take decisions clearly. The accurate data is helpful for both clinicians and patients for identifying the individual risk. The K Nearest and K Means algorithm are used for partitioning number of observations with nearest mean value which means classify a given data set through a certain number of clusters. This aim is to minimizing an objective function and gives a safety measure for affected persons. It compares every healthcare detail with original www.ijaers.com
dataset and provides an accurate result and gives an alert to the affected persons. II. EXISTING SYSTEM In exiting approach syndicates K Nearest Neighbor and genetic algorithm to expand the classification accurateness of heart disease data set. They used genetic search as a heavens measure to crop redundant and immaterial attributes and to rank the attributes which contribute more towards classification. Least graded attributes are detached and classification algorithm is built based on estimated attributes. This classifier is accomplished to categorize heart disease data set as either healthy or sick. In exiting paper recommended for only classification not a prediction so some safekeeping issues is accrued. In existing system Old genetic algorithm are used, so there is no prediction it leads to the low security of the system. III. PROPOSED SYSTEM In exiting system only proposed for classification technique. In this paper proposed classification and prediction of K Nearest Neighbor with K Means classification. This combined approach of K Nearest Neighbor and K-Means clustering to improve the classification accuracy of heart disease data set and the prediction can be used to provide the security in heart disease medical data. The proposed system works as follows Proposed algorithm Step 1) Data set are loaded Step 2) Attributes are ordered based on their value Step 4) selects the subset of higher ranked attributes Step 5) Apply (KNN+K-Means) on the subset of attributes that exploits Classification accuracy Step 6) Estimate the correctness of the classifier, which dealings the ability of the Classifier to properly categorize unfamiliar sample. Accuracy of the classifier is computed as Accuracy = Page | 30