PARTICLE SWARM OPTIMIZATION ASSISTED SUPPORT VECTOR MACHINE BASED DIAGNOSTIC SYSTEM FOR LUNG CANCER PREDICTION AT THE EARLY STAGE

Abstract

Cancer is one of the countries' deadliest illnesses and it will cure if diagnosed early. Lung cancer is the main cause of death in humans, as the signs of lung cancer occur in advanced stages, so it is difficult to diagnose and contributes to high mortality among other forms of cancer. Therefore, early prediction of lung cancer is mandatory for the diagnostic process and offers better odds of effective therapy. Researchers focus on healthcare to diagnose and avoid lung cancer early. Medical data has achieved its full capacity by offering large data sets to researchers. . Machine learning is a division of artificial intelligence that utilizes a range of mathematical, probabilistic, and optimization techniques that allow computers to "learn" from past examples and identify correlations that are difficult to distinguish from big, noisy, or complex data sets. Machine Learning is commonly utilized in the detection and prognosis of lung cancer. In this paper, Particle Swarm Optimization assisted Support Vector Machine based Diagnostic System for Lung Cancer prediction at an early stage is proposed. The primary objective of this paper is to evaluate the effect of the PSO and SVM for mining the lung cancer dataset  The aim of this paper is to improve the accuracy of the machine learning algorithm. The proposed technique was also verified by using the various standard lung cancer classification data sets. The comparison is drawn among the proposed and the existing technique based upon the various standard quality of service parameters. Experimental results indicate that the proposed algorithm is more efficient than existing techniques..