C4.5 Algorithm Based on Forward Selection and Particle Swarm Optimization for Improving Accuracy in Heart Disease Patient Classification

Early detection of heart disease is crucial given the high number of cases occurringin advanced stages and affecting individuals in their productive years. Utilizing data mining, the C4.5 Algorithm is one method capable of detecting the onset of heart disease, prompting timely awareness and early prevention. The dataset employed is the Heart Disease Cleveland UCI from Kaggle, featuring 13 input attributes and 1 target attribute. Using the Decision Tree method results in decision-making by constructing a decision tree. The test outcomes revealed an accuracy rate of 77.11% with the C4.5 algorithm, 83.69% with the C4.5 algorithm employing Forward Selection, and 84.73% with the C4.5 algorithm based on Forward Selection and Particle Swarm Optimization.