Diagnosis of Coronary Artery Diseases using Classification Algorithms Based on Wavelet Transforms
Rajesh Kumar T.1, Srinivasa rao A.2, Ashok B.3, Rajesh Kumar E.4
1Rajesh Kumar T., Department of CSE, K L E F, Vaddeswaram, Guntur, Andhra Pradesh, India.
2Srinivasa Rao A., Department of CSE, K L E F, Vaddeswaram, Guntur, Andhra Pradesh, India.
3Ashok B., Department of CSE, K L E F, Vaddeswaram, Guntur, Andhra Pradesh, India.
4Rajesh Kumar E., Department of CSE, K L E F, Vaddeswaram, Guntur, Andhra Pradesh, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3122-3126 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7836078919/19©BEIESP | DOI: 10.35940/ijitee.I7836.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Index Terms: Heart Disease, Wavelet Transform, Haar WT, Coronary Artery Diseases (CAD)
Scope of the Article: Design and Diagnosis