ANALYSIS USING ANT COLONY OPTIMIZATION TECHNIQUES TO MINE LUNG CANCER DATA FOR THE PURPOSE OF INCREASING OR DECREASING THE DISEASE PREDICTION VALUE

Abstract

The leading cause of death for both men and women is cancer. The unchecked proliferation of aberrant cells that begin in one or both lung most often in the cell lining the air passageways. Small cell lung cancer and non-small cell lung cancer are the two primary forms. Non smokers Get Lung Cancer at a Rate of 10–15%. Smokers Make Up 50% of the Case. The longer someone smokes and the more cigarettes they smoke, the higher their risk of developing lung cancer. Lung cancer has become more common. Age, sex, wheezing, shortness of breath, and chest pain are among the symptoms that can indicate a patient's likelihood of developing lung cancer. Data mining algorithms, such as classification, decision tables, naïve-based, ant colony optimization, lung cancer prediction, and data mining techniques, are used to detect lung cancer disease in its early stages. According to this paper, early detection of lung cancer can completely cure the disease and help doctors save patients' lives. Ant colony optimization data mining techniques are useful for improving or decreasing the disease prediction value of lung cancer data.