Shobeetha Manirajan, Dr.A.Shaik Abdul Khadir


Cardiac Attacks make worse and threatens the middle aged persons in the world. There are lot of researches are undergoing to make control the cardiac heart attacks. It is difficult to predict the causes and possibilities of heart attacks based on the historical patient records. In data mining, the existing techniques like Decision Tree Classification, Naïve Bayesian Algorithm are used to predict the possibilities of heart attacks and their symptoms. However it works well for the structured data. It will not evaluate the unstructured heterogeneous data from various resources. In this proposed work, the analysis performance should be improved to obtain the accurate results. To produce a mechanism for perform predictive analysis about the medical treatments through unstructured heterogeneous data using K Means clustering and SVM Classification. Here the system retrieves the data from the cloud storage. To optimize the data retrieval, here the framework implemented with the Artificial Bee Colony Optimization Algorithm. This helps to obtain the classified results for the preventive measures through the factors from historical data from various cloud resources. To improve the improvements in the analysis based on the mechanism implementation results should be compared with the existing algorithms using UCI dataset of heart attacks in weka tool.


K Means Clustering, Decision Tree Classification, Naïve Bayesian Classification, SVM classification.

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DOI: https://doi.org/10.26483/ijarcs.v8i9.5034


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