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Heart disease is a disease with a high mortality rate, there are 12 million deaths each year worldwide. This is what causes the need for early diagnosis to find out the heart disease. But the process of diagnosis is quite challenging because of the complex relationship between the attributes of heart disease. So it is important to know the main attributes that are used as a decision making process or the classification process in heart disease. In this study the dataset used has 57 types of attributes in it. So that reduction is needed to shorten the diagnostic process, the reduction process can be carried out using the Principal Component Analysis (PCA) method. The PCA method itself can be combined with data mining calcification techniques to measure the accuracy of the dataset. This study compares the accuracy rate using the C5.0 algorithm and the Naïve Bayes Classifier (NBC) algorithm, the results obtained both after and before the reduction are Naïve Bayes Classifier (NBC) algorithms that have better performance than the C5.0 algorithm
Utomo et al. (Sat,) studied this question.