This document summarizes a research paper that proposes a machine learning model to predict heart disease using classification algorithms. The paper uses the Cleveland heart disease dataset to train and test decision tree, random forest, and a hybrid model combining the two. Experimental results showed the hybrid model achieved 88.7% accuracy in predicting heart disease, outperforming the individual algorithms. The paper aims to develop an effective heart disease prediction tool to assist healthcare professionals.