People suffering from Lung cancer are commonly found throughout the world. There are many people who die of this fatal ailment every year. Even though there are many reasons for the occurrence of this disease, it is difficult to say if a person is suffering from lung cancer, unless we test for it. This is costly. It would be highly useful if we could identify significant symptoms in a person that could help to predict the possibility of occurrence of this disease in them. With this objective in mind, we have used a dataset consisting of values of 16 significant symptoms in people who were diagnosed and tested for lung cancer. Based on knowledge of various factors leading to lung cancer and the general symptoms in people suffering from lung cancer, we have chosen these biomarkers. Statistical techniques were used to study and analyze the occurrence of each symptom / factor in the sample population with and without lung cancer, to arrive at a set of predominant symptoms in people with lung cancer. The dataset was also tested for significance of factors using a data mining tool. The dataset was classified using various decision tree algorithms and the results were compared. Decision rules were also generated using Apriori Analysis. This was used to build three data models that help to predict the occurrence of lung cancer among patients with significant symptoms. A maximum accuracy of prediction of 93% was achieved. This was found to be better than prediction through decision tree classifiers.

Data Mining, Classification, Prediction, Lung Cancer, Apriori Analysis

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