PREDICTION OF HEART DISEASE CLASSIFICATION BY RANDOM FOREST MODEL BASED ON GREY WOLF OPTIMIZATION ALGORITHM

This paper proposes a random forest model based on the gray wolf optimization algorithm to study the classification of heart disease, and compares it with the prediction results of decision tree and basic random forest. The results show that the random forest using the optimization algorithm has better prediction results, its accuracy is 5.2% higher than that of ordinary random forest, which provides a certain reference for the prediction and prevention of heart disease in the future.