Optimization Techniques used in Heart Disease Prediction: A Systematic Analysis

Today, heart disease is one of the leading causes of death around the world, making it a top priority in healthcare systems everywhere. Predicting cardiac issues with precision is crucial for facilitating prompt medical attention. There is great potential for improved decision-making and data-driven forecasts in healthcare with the application of Particle Swarm Optimization (PSO) in conjunction with machine learning. By utilizing numerous algorithms, this method equips both patients and doctors to intervene early and avoid sickness. It's important to remember that the parameters you pick for your optimization strategy can have a significant impact on how well these models work. This research compares three popular optimization techniques—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization—in the context of predicting cardiovascular disease. The fundamental goal of this study is to compare and contrast several optimization strategies, such as Feature Selection, Bayesian optimization, Evolutionary algorithms, and their hybrids, and other tried-and-true approaches from current scholarly literature. The overarching goal of these studies is to develop concepts for better heart illness diagnostics. The research also compares other Machine Learning methods developed to solve specific optimization problems. It is hoped that the knowledge gathered from this study would encourage further study into optimizing methods for cardiac illness diagnostics and the development of more efficient preventative measures.