Improving prediction analysis outcomes by the use of Cluster-based SVM classification
DOI:
https://doi.org/10.29070/592f4a43Keywords:
Medical, cardiovascular disease, Ant Bee colony optimization, SVM, PredictionAbstract
The majority of people in today's society die from cardiovascular disease. Making a correct medical diagnosis is a complex but crucial process that requires speed & precision. It is recommended that the analytical performance be enhanced in order to achieve precise results in this proposed study. With the use of K-means clustering & support vector machine classification, develop a system that can analyse unstructured heterogeneous data for medical treatment predictions. the system retrieves data from the cloud, and the framework uses the ABC Optimisation Algorithm to make the data retrieval process more efficient. This is useful for getting the categorised outcomes for preventative actions from different cloud resources' historical data, and for comparing the results of the mechanism's implementation with the algorithms that already exist using the UCI dataset of heart attacks in the weka tool, so that the analysis can be improved.
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