Classification Methods for Lung Cancer Using Machine Learning

Abstract

Lung cancer is the most dangerous disease in the world. According to WHO (World Health Organization) millions of peoples loses their life due to lung cancer. The spread of lung cancer is increasing day by day. The one more essential reason of increasing lung cancer is that it can’t be detected in its early stages. So many researchers gave different techniques to detect lung cancer in its very early stage. But machine learning is the most efficient technique because its computational capability is very high. Through detailed data processing this approach easily forecasts the illness. In this paper we examine different computer classifier learning technologies for the data available in the UCI machine learning repository on available lung cancer. The knowledge is predisposed and modified to a paired configuration and a noteworthy classifier system in weka apparatus is used to group the information set into cancer and non-cancerous. The analysis process indicates that the proposed Multilayer perceptron classification has achieved an unprecedented 98.69 percent precision and is seen as the feasible classification tool for expectations of Lung malignancy knowledge.