Nowadays, medical diseases are one of the primary causes of death, and it is one the major concerns of developed countries. So, the disease identification process needs a lot of attention since if the diseases are idenfied at the early stage, the rate of death can be decreased. Machine learning techniques is one of the popular approaches that is used for identifying the diseases at the early stage. In this paper, two machine learning techniques, namely Naive Bayes classification algorithm and Laplace smoothing technique are used to predict the heart disease. Here, many medical details are used, such as gender, age, fasting blood sugar, blood pressure, cholesterol, etc. to predict the hearth disease of a patient. The proposed decision system supports avoiding unnecessary diagnosis test, which can be highly beneficial to start the treatment quickly. Thus, both time and money can be saved. Both the performance analysis and the experimental results show the efficiency of the proposed scheme over the existing schemes.
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