Lung Cancer Prediction using Data Mining Techniques

Abstract

The major cause for death in human beings is because of cancer .Lung cancer is one of the most common and serious types of cancer that severely harms the human body. In order to cure the cancer early cancer detection is required. If lung cancer is diagnosed at early stages many lives will be saved. The other name for lung cancer is lung carcinoma, an uncontrolled malignant tumor distinguished by undisciplined cell growth in lung cells. There are many people suffering from this kind of cancer and confining to death. If this is left untreated, this may grow later than lung by metastasis into other parts of body. Many of the cancers starts from lungs, called as primary lung carcinoma. There are two types of small cell lung carcinoma (SCLC), non small cell lung carcinoma(NSCLC). The main reason for lung cancer is smoking of cigarette. There are many researches targeting on exact approaches for treating cancer. To predict the survival rate for NSCLC patients data mining techniques can be used with selection of algorithms. The algorithms used to detect the lung cancer are Support vector machine (SVM), Decision tree, k-Nearest neighbour, Random forest, Logistic regression. In this paper By implementing 2 different datasets and various packages and libraries in python, it is compared and on implementation found suitable algorithms have more accuracy on certain data sets for optimum prediction rate of lung cancer..

Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models

“…In 2018, an automatic nodule region detection method based on artificial intelligence and image processing techniques was developed using lung tomography. In this context, a method and system that provide automatic detection and diagnosis of nodule regions on lung CT cross section images have been developed [ 10 , 11 ].…”