Prediction of Lung Cancer Using Classifier Models

Shamreen Fathima Saddique, Sharmithra P, Justin Xavier D

Abstract: In recent years, Lung Cancer has become a serious disease that threaten the health and mind of human. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes a lung cancer prediction model based on nave Bayes which aims at analyzing some readily available indicators (age, smoking, alcohol consumption, chest pain, etc. ) effects on lung cancer and discovering some rules on given data. The method can significantly reduce the risk of disease through digging out a clear and understandable model for lung cancer from a medical database. naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes & #039, theorem with strong (naive) independence assumptions between the features. The validation of results at Chennai Port Hospital shows that the Nave Bayes algorithm can greatly reduce the problem and it can effectively predict the impact of these readily available indicators on the risk of lung cancer. Additionally, we get a better prediction accuracy using Nave Bayes than using the support vector machine algorithm, logistic regression and random forest

Keywords: prediction model, Nave Bayes, Lung Cancer