Study of Heart Disease Diagnosis by Comparing Various Classification Algorithms
Ajit Solanki1, Mehul P. Barot2
1Ajit Solanki, Research Scholar, Department of Computer Engineering, LDRP Institute of Technology and Research, Gandhinagar (Gujarat), India.
2Mehul P. Barot, Assistant Professor, Department of Computer Engineering, LDRP Institute of Technology and Research, Gandhinagar (Gujarath), India.
Manuscript received on 10 January 2019 | Revised Manuscript received on 20 January 2019 | Manuscript Published on 30 January 2019 | PP: 40-42 | Volume-8 Issue-2S2, January 2019 | Retrieval Number: 100.1/ijeat.B10090182S219/19©BEIESP
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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: In the survey paper, different techniques of mining for forecasting of heart risk are discussed. Heart disease cause millions of death every year, It’s rapidly increasing. Mining methods are too much helpful detect and diagnose heart risk. Data mining in medical domain has great potential to uncover the pattern which are hidden in the medical dataset . For this reason, different mining methods can be used to abstract knowledge for forecasting heart disease . In this paper, survey is carried on various single data mining techniques and hybrid mining techniques to identify the best suited technique to achieve high accuracy in prediction of heart disease . Here, Potential of many classification techniques was evaluated, namely Naïve Bayes, SVM, Decision tree, K-nearest neighbour, and even hybrid approach of classifiers. Analysis on various methods proved that techniques based on classification obtain high accuracy compared to previous methods .
Keywords: Data Mining, Classification, Disease Diagnosis, Prediction, Accuracy.
Scope of the Article: Classification