Early Lung Cancer Prediction Using Neural Network with Cross-validation

Abstract

Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung cancer is caused generally by smoking and the use of tobacco products. It is classified into two broad Small-cell lung Carcinomas and non-Small cell lung carcinomas. Lung cancer treatments include surgery, radiation therapy, chemotherapy, and targeted therapy. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by k-fold cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance. Experimental results conclude that analyzing interfering causes of lung cancer can effectively accomplish disease classification model with an accuracy of 95%.