Efficient Classification of Heart Disease using KMeans Clustering Algorithm

Efficient Classification of Heart Disease using KMeans Clustering Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-12
Year of Publication : 2020
Authors : M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, M. Ranjitha, P. Malarkodi, S. Nallusamy
DOI :  10.14445/22315381/IJETT-V68I12P209

Citation 

MLA Style: M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, M. Ranjitha, P. Malarkodi, S. Nallusamy. Efficient Classification of Heart Disease using KMeans Clustering Algorithm International Journal of Engineering Trends and Technology 68.12(2020):48-53. 

APA Style:M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, M. Ranjitha, P. Malarkodi, S. Nallusamy. Efficient Classification of Heart Disease using KMeans Clustering Algorithm.  International Journal of Engineering Trends and Technology, 68(12), 48-53.

Abstract
Arteria Coronaria Heart Disease (CAD) is brought about by atherosclerosis in coronary supply routes and consequences in heart failure and besides respiratory failure. For the conclusion of CAD, angiography is utilized as an expensive tedious, and profoundly specialized obtrusive strategy. Scientists are consequently provoked for elective techniques, for example, Artificial Intelligence (AI) calculations that could utilize non-obtrusive clinical information for the coronary illness analysis and evaluating its seriousness. This research illustrates a technique crossbreed strategy intended for CAD determination, containing hazard factor recognizable proof utilizing particle swam optimization with component subset and Kmeanss scheme. This implementation compares Multi-Layer Perceptron (MLP), Multinomial Strategic Relapse (MLR), Fluffy Unordered Standard Acceptance Calculation (FURIA), and C4.5 for CAD disease detection. MLR beats different procedures. The proposed hybridized model improves the precision of characterization calculations is 11% for the Cleavelanddata. The anticipated strategy is, along these lines, a capable apparatus for recognizable proof of CAD affected role with progress forecast exactness.

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Keywords
Machine Learning, Heart Disease, Risk Prediction, Feature Selection, Prediction Model, Classification Algorithms, Cardiovascular Disease.