Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

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Aim:

To apply machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease

Existing System:

The obtained results are compared with the results of existing models within the same domain and found to be improved. The data of heart disease patients collected from the UCI laboratory is used to discover patterns with NN, DT, Support Vector machines SVM, and Naive Bayes. The results are compared for performance and accuracy with these algorithms. The proposed hybrid method returns results of 86:8% for F-measure, competing with the other existing methods.

Proposed System:

We have used python and pandas operations to perform heart disease classification of the Cleveland UCI repository. It provides an easy-to-use visual representation of the dataset, working environment and building the predictive analytics. ML process starts from a pre-processing data phase followed by feature selection based on data cleaning, classification of modeling performance evaluation, and the results with improved accuracy.