Özet:

We proposed as checking the whole patient heart disease using naïve Bayes classification in machine learning. So in that, we will take results of how much percentage patients get disease as a positive information and negative information. Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages. So we can use machine learning. The proposed shows a machine learning processing model, from the data mining perspective. Using classifiers, we are processing heart percentage and values are showing as a confusion matrix. We proposed anew classification scheme which can effectively improve the classification performance in the situation that training dataset is available. Stent diagnosis of heart disease. Furthermore, the resulting model has a high specificity rate which makes it a handy tool for junior cardiologists to screen out patients who have a high probability of having the disease and transfer those patients to senior cardiologists for further analysis.

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