A Hybrid Fish – Bee Optimization Algorithm For Heart Disease Prediction Using Multiple Kernel SVM Classifier
T. Keerthika1, K. Premalatha2
1T. Keerthika, Assistant Professor, Department of Information Technology Krishna College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
2T. Keerthika, Professors, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam (Tamil Nadu), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 729-737 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11520789S219/19©BEIESP DOI: 10.35940/ijitee.I1152.0789S219
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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: The patient’s heart disease status is obtained by using a heart disease detection model. That is used for the medical experts. In order to predict the heart disease, the existing technique use optimal classifier. Even though the existing technique achieved the better result, it has some disadvantages. In order to improve those drawbacks, the suggested technique utilizes the effective method for heart disease prediction. At first the input information is preprocessed and then the preprocessed result is forwarded to the feature selection process. For the feature selection process a proficient feature selection is used over the high dimensional medical data. Hybrid Fish Bee optimization algorithm (HFSBEE) is utilized. Thus, the proposed algorithm parallelizes the two algorithms such that the local behavior of artificial bee colony algorithm and global search of fish swarm optimization are effectively used to find the optimal solution. Classification process is performed by the transformation of medical dataset to the Multi kernel support vector machine (MKSVM). The process of our proposed technique is calculated based on the accuracy, sensitivity, specificity, precision, recall and F-measure. Here, for test analysis, the some datasets used i.e. Cleveland, Hungarian and Switzerland etc., that are given based on the UCI machine learning repository. The experimental outcome show that our presented technique is went better than the accuracy of 97.68%. This is for the Cleveland dataset when related with existing hybrid kernel support vector machine (HKSVM) method achieved 96.03% and optimal rough fuzzy classifier obtained 62.25%. The implementation of the proposed method is done by MATLAB platform.
Keywords: Artificial bee Colony Algorithm, Fish Swarm Optimization, Multi kernel Support vector Machine, Optimal Rough Fuzzy, Cleveland, Hungarian and Switzerland.
Scope of the Article: Fuzzy Logics