Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique.

Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.