Early Detection of Lung Cancer Risk Using Data Mining


Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of geneticas well as environmental factors is very important in developing novel methods of lung cancer prevention.However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed whichis easy, cost effective and time saving. Materials and
Methods: Initially 400 cancer and non-cancer patients’data were collected from different diagnostic centres, pre-processed and clustered using a K-means clusteringalgorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered usingAprioriTid and a decision tree algorithm.
Results: Finally using the significant pattern prediction tools for alung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful indetection of a person’s predisposition for lung cancer.
Conclusions: Most of people of Bangladesh do not evenknow they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible.Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effectivepreventive strategy.