Detection of Lung Disease using Novel Genetic Algorithm in comparison with Particle Swarm Optimization to improve Accuracy

Abstract

Aim: The study aimed to predict lung cancer and improve accuracy by comparing the Novel Genetic Algorithm(GA) algorithm with the particle swarm optimization (PSO) algorithm. Materials and Methods: Proposed work involves two groups for lung cancer detection. Group 1 was the Novel Genetic Algorithm (GA) algorithm and Group 2 was the particle swarm optimization (PSO) algorithm. The sample size was calculated. It was identified that 20 samples/group and 40 samples were taken totally. The improved GA algorithm was compared with the particle swarm optimization (PSO) algorithm for predicting the accuracy. Results and Discussion: Data collection was carried out the analysis can be done in the compiler for the execution of result and accuracy of a particular Algorithm. Here in this proposed work, the improved Novel Genetic Algorithm(GA) algorithm was compared with the particle swarm optimization (PSO) algorithm. Conclusion: The data was collected from various resources for the usage of lung cancer prediction system The Improved Novel Genetic Algorithm(GA) algorithm was used for the whole lung cancer prediction process for more accurate results compared to the particle swarm optimization (PSO) algorithm.