Data mining is the process of rearranging through large datasets to identify patterns and establish relationships between them to solve problems through data analysis. Data mining tools allow enterprise to predict future trends. A pattern is useful, interesting and easily understood by human if it is valid for a given test and with some degree of certainty. Though the data amount generated in predicting heart disease is huge and complex advance data mining techniques can process the data. Heart disease is one the disease that causes the maximum causalities. This problem is identified long before but no proper actions been taken to combat this problem. This paper set out goal to finding which method would be best for predicting the diseases using data of four different dataset from four different places. Therefore, this article tries to finding which method would be best for predicting the diseases using data of four different datasets from four different places. This is a comparative study on the efficiency of different data mining techniques such as Decision Tree (DT), K-Nearest Neighbor (kNN), Naive Bayes, Logistic Regression in predicting heart diseases. The Data Mining techniques are analyzed and the accuracy of prediction is noted for each method used. The result showed that heart diseases can be predicted with accuracy of above 80%.