Methods for Medical Data Mining Based Metaheuristic Algorithms

Abstract

In recent years, the rapid accumulation of medical data has presented both opportunities and challenges in healthcare. Medical data mining, a field at the intersection of data science and healthcare, aims to extract valuable insights from these vast datasets to improve patient care, disease diagnosis, and medical research. Traditional data mining techniques often face limitations when dealing with the complexity and scale of medical data. Met heuristic algorithms, inspired by natural processes such as evolution and swarm intelligence, have emerged as powerful tools for tackling these challenges. This paper presents an overview of methods for medical data mining based on metaheuristic algorithms. We discuss the unique advantages offered by metaheuristic approaches, including their ability to efficiently explore large solution spaces and find high-quality solutions in complex optimization problems. Specifically, we examine several popular metaheuristic algorithms commonly applied in medical data mining, including genetic algorithms, particle swarm optimization, simulated annealing, ant colony optimization, and evolutionary strategies.