The goal of the research was to compare different types of decision tree algorithms using medical data. The researchers focused on analyzing the performance of a weighted decision tree algorithm called C4.5. They found that the C4.5 decision tree algorithm had the highest accuracy of 71.42% for medical data and 48.69% for real-world data. The study suggests that decision tree algorithms, especially C4.5, are effective for solving medical data problems like classifying heart disease.