Recently, heart disorders have taken the position of being the main cause of death. It is expected that this trend will be in place for the foreseeable future. Thus, doing an accurate diagnosis of heart disease turns out to be a top priority to help gain an improvement in prognosis for the patients while reducing the rates of mortality involved in the condition. Coronary artery disease and chronic heart failure are some of the most common reasons behind heart attacks. Of all the diagnostic procedures applied to determine whether a patient has heart disease, an angiography is the most frequently used. Actually, it is true that researchers today are channeling an immense proportion of their efforts toward cardiovascular disorders. This led to the establishment of a fruitful diagnostic strategy due to the introduction of techniques from artificial intelligence, such as machine learning. The use of machine learning at any stage in the diagnostic process produces accurate and efficient results that therefore contribute to timely heart disease detection. Through the usage of our research project in this research paper, we will be able to reach the objective of achieving an accurate measurement of the prediction of heart disease. Besides two other methods, we also made use of the dataset that was available from the University of California, Irvine and used logistic regression. Using logistic regression, we could attain some degree of accuracy of 92.3–92.3%.