Detection of Septic Arthritis using Meta Heuristic Algorithms

Abstract

Arthritis is one of the kinds of chronic disease which causes inflammation to the joints which causing pain and stiffness that can worsen with aging. Mostly it is caused due to the decrease of Cartilage thickness present between the bone joints. There are more than 100 types of arthritis exist in the world. In general arthritis is classified into two parts, septic arthritis and reactive arthritis. Septic arthritis is an unfavorable arthropathy originated by an intraarticular infection which is usually connected to severe symptoms such as pain and decreased the range of motion. In this work, an analysis has been done on two meta-heuristic methods for early detection of septic arthritis since it is a direct invasion of bacteria. In this paper two different meta-heuristic methods like Ant Colony Optimization (ACO) and Clown Fish Queuing and Switching Optimization Algorithm (CFQSOA) are analyzed for the early detection of septic arthritis. By the early diagnosis and treatment of arthritis, the damage to the joints can be reduced.