Prediction of Lung Cancer using Data Mining Techniques

At present, most people suffer a tumor due to their habitual attitude and food habits. The result of this disease is death, and every day the ratio of morbid and mortality is gradually increasing. Although the world has been substantial development in medical field, till date lung cancer is the one of the most dangerous diseases that spikes in this world. Lung cancer is also known as carcinoma. This sort of disease arises due to the abnormal growth of the tumour cells in human body. Lung cancer is categorized into Small Cell Lung Carcinoma (SCLC) and Non-Small Cell Lung Carcinoma (NSCLC). Lung cancer is mostly caused due to smoking. Such a disease needs to be predicted in advance to take the necessary remedial action against this disease. Despite all solutions attained by the previous research, they have all proven to be inadequate for the detection of the disease at an earlier stage. Therefore, this research work addresses the prediction of NSCLC using data mining methods. To detect lung cancer, the data mining algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbour, Decision Tree and Support Vector Machines have been proposed. This work has been accomplished by implementing two datasets, several packages and python library functions with optimum prediction ratio of lung cancer.