TRENDS ON MACHINE LEARNING TECHNIQUES USED IN MEDICAL FOR DISEASES DETECTION

Abstract: The branch of computer science that focuses on creating machines that behave like humans is called artificial intelligence (AI). Medical disease detection is a rapidly expanding field of study in artificial intelligence. Many efforts have been made in recent years to enhance medical disease detection since mistakes and issues with this process can result in grave medical errors and incorrect treatment. In the field of biomedicine, meta-heuristic approaches have been widely used to identify medical conditions and offer improved perception and prognostication accuracy. Consequently, Swarm intelligence has primarily been used to address the various types of optimization problems due to the versatility of numerical experimentation. Nonetheless, despite the widespread use of Swarm intelligence techniques for disease detection, a gap remains in the comparative survey. This paper provides an overview of the different approaches used in medical disease diagnosis. The goal of this study is to provide a thorough literature review of the most recent methods for disease detection knowledge discovery. While the main objective is to provide directions for future enhancement and development in this area, the systematic analysis also reveals research gaps related to Swarm intelligence strategies. This paper provides an organized overview of the conceptual model for advanced research that has been studied thus far in the designated literature. The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles and synthesizes papers from the 2020-2024 to assess trends, effectiveness, and future directions.