About

We’re exploring how machine learning helps predict heart disease. We start with decision tree classification, refining it through pruning and optimizing parameters like Gini impurity and information gain. Despite our efforts, the standout performer is the Random Forest model. Unlike a single decision tree, Random Forest builds multiple trees and combines their predictions, resulting in more accurate outcomes. With an impressively low out-of-bag error rate of 16.89%, Random Forest excels in predicting heart disease. This advancement in accuracy enhances our ability to anticipate heart conditions, leading to better healthcare decisions and outcomes.