Cardiovascular Disease Prediction and Classification using Modified Teaching Learning Optimization Method
N. Rajinikanth1, L. Pavithra2

1N. Rajinikanth*, Department of Computer Applications, CMS College of Science & Commerce, Coimbatore, India.
2Dr. L. Pavithra, Department of Computer Science, Dr. N. G. P. Arts & Science College, Coimbatore, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on March 01, 2020. | Manuscript published on March 10, 2020. | PP: 808-813 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2559039520/2020©BEIESP | DOI: 10.35940/ijitee.E2559.039520
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Abstract: Cardiovascular disease (CVD) is possibly the greatest reason for casualty and death rate among the number of inhabitants on the planet. Projection of cardiopathy is viewed as one of the most crucial subjects in the area of clinical records exploration. The measure of information in the social insurance industry is massive. The Data mining process transforms the huge range of unrefined medical service data into meaningful information that can lead to erudite decision and projection. Some recent investigations have applied data exploratory procedures too in CVD estimation. However, only very few studies have revealed the elements that play crucial role in envisioning CVDs. It is imperative to opt for the combination of correct and significant elements that can enhance the functioning of the forecasting prototypes. This study aims to ascertain meaningful elements and data mining procedures that can enrich the correctness of foretelling CVDs. Prognostic models were formulated employing distinctive blend of features selection modified teaching learning optimization techniques, SVM and boosting classification. Here the proposed strategy gives high precision outcomes with existing classification. 
Keywords: Feature Selection, Heart Disease Prediction, Modified Teaching Learning Optimization, Svm Classification, Adaboosting.
Scope of the Article:  Classification