Open AccessDiagnosis of Coronary Artery Diseases using Classification Algorithms based on Wavelet Transforms
Author(s) -
Rajesh Kumar T,
Srinivasa Rao A.,
B. Ashok,
Rajesh Kumar E.
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7836.078919
Subject(s) - random forest , cad , artificial intelligence , algorithm , computer science , coronary artery disease , decision tree , pattern recognition (psychology) , wavelet , machine learning , mathematics , cardiology , medicine , engineering , engineering drawing
One of the primary drivers of the death in the world is Coronary Artery Diseases (CAD) which is a major threat in developing and developed countries. The fundamental drivers in CAD leads to blockage of the coronary lumen subsequently blood clot and that prompts to damage of heart muscles or unexpected heart attack which causes death. It is difficult to ascertain that a certain person has been affected by CAD, since there are bunch of parameters has been involved to ascertain the conclusion. Classification has been done using wavelet transform to classify the certain parameters. We analyzed following methods such as NB, Logistic, SMO, RBF Network, K-star, Multiclass Classifier, Conjunctive rule, Decision table, LMT, NB Tree, DTNB, LAD Tree, Random Tree and Random Forest calculations has been associated with extensive fragment of the surveys. This database has been generated from UCI machine learning database. In this paper, we used k-fold cross validation with k values as 10, with 14 properties and calculations of Accuracy, Precision, TPR, FPR, Recall, F-measure and ROC are analyzed practically. The experimental evaluation shows the improvement in accuracy rate of 77.0%, by using the Logistic, SMO and LMT algorithms than the traditional method.