Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques
Dakhaz Mustafa Abdullah, Adnan Mohsin Abdulazeez, Amira Bibo Sallow
Abstract
Lung cancer is one of the leading causes of mortality in every country, affecting both men and women. Lung cancer has a low prognosis, resulting in a high death rate. The computing sector is fully automating it, and the medical industry is also automating itself with the aid of image recognition and data analytics. This paper endeavors to inspect accuracy ratio of three classifiers which is Support Vector Machine (SVM), K-Nearest Neighbor (KNN)and, Convolutional Neural Network (CNN) that classify lung cancer in early stage so that many lives can be saving. Basically, the informational indexes utilized as a part of this examination are taken from UCI datasets for patients affected by lung cancer. The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. The experimental results show that SVM gives the best result with 95.56%, then CNN with CNN 92.11% and KNN with 88.40%.