Pneumonia Prediction Using Swarm Intelligence Algorithms

In this chapter, a combination of swarm intelligence algorithms is used to diagnose pneumonia from a patient's x-ray report of lungs conditions. The ability of swarm intelligent algorithms to solve a wide range of problems. For the classification of the disease for this research, a feed forward neural network with swarm intelligent algorithms had been used. The capabilities of global optimization learning algorithms were investigated, along with their training and testing results. In the Chest X-Ray Images (Pneumonia) dataset with categorical and binary data, these optimizations comprise Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Glow worm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA). The findings could help researchers quickly find the best algorithm for use in a Pneumonia medical dataset, with final accuracy ranging from 85 to 95 percent after all five final epochs.