Heart Disease Prediction

 
 
 
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  • Abstract


    Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From  the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared.

     


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  • References


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      [4] Jianxin Chen, Guangcheng Xi, Yanwei Xing, Jing Chen, and Jie Wang 2007 “Predicting Syndrome by NEI Specifications: A Comparison of Five Data Mining Algorithms in Coronary Heart Disease” Life System Modeling and Simulation Lecture Notes in Computer Science, pp 129-135.

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      [6] Mai Shouman, Tim Turner, Rob Stocker 2012 “Using Data Mining Techniques In Heart Disease Diagnoses And Treatment“ Electronics, Communications and Computers (JECECC), 2012 Japan-Egypt Conference March 2012, pp 173-177.

      [7] Robert Detrano, Andras Janosi, Walter Steinbrunn, Matthias Pfisterer, Johann-Jakob Schmid, Sarbjit Sandhu, Kern H. Guppy, Stella Lee, Victor Froelicher 1989 “International application of a new probability algorithm for the diagnosis of coronary artery disease” The American Journal of Cardiology, pp 304-310.15

      [8] Polat, K., S. Sahan, and S. Gunes 2007 “Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing” Expert Systems with Applications 2007, pp 625-631.


 

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Article ID: 16494
 
DOI: 10.14419/ijet.v7i3.12.16494




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