Open AccessHeart Disease Prediction
Author(s) -
S. Vinothini,
Ishaan Singh,
Sujaya Pradhan,
Vipul Sharma
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.12.16494
Subject(s) - cluster analysis , decision tree , support vector machine , computer science , decision tree learning , data mining , cluster (spacecraft) , machine learning , artificial intelligence , set (abstract data type) , heart disease , regression , tree (set theory) , pattern recognition (psychology) , mathematics , statistics , medicine , cardiology , programming language , mathematical analysis
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.