A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR RISK PREDICTION: SUPPORT VECTOR MACHINE, GRADIENT BOOSTING, AND ROTATION FOREST

Abstract

Cardiovascular heart disease is one of the most fatal problems in the world and is a major cause of deaths globally, reaching around 17.9 million deaths every year. Timely prediction of heart disease is critical for instant response to achieve favorable outcomes; therefore, it requires accurate diagnosis at the right time. Today, the healthcare field has a lot of data, but not much enough knowledge. Machine learning allows computer programs to learn from existing data, get better at doing tasks through experience without needing help from people, and then use what they have learned to make smart choices. There are many different methods and tools in data mining and machine learning that can be used to get useful information from databases and to apply that information for better and more accurate diagnosis. In this research, we compared three machine learning algorithms—Support Vector Machine, Gradient Boosting, and Rotation Forest—to find out which one works best for predicting heart diseases on time. We looked at how accurate each method was, and both Rotation Forest and Gradient Boosting were the most accurate.