Informatics Publishing Limited

Abstract

Cancer is a one of the deadly diseases. Detection of the cancer in the initial stage will be helpful for curing cancer completely. A disease that is commonly make an incorrect diagnosis is lung cancer. Many of their lives was saved because of the earlier diagnosis of lung cancer, if it is not which may lead to other critical problems causing sudden death. It’s accurate and prediction depends mainly on the early detection and diagnosis of the disease. One of the most common mistakes of medical malpractices globally is an error in diagnosis. Knowledge can be derived from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification-based data mining techniques such as Rule Based, Decision tree, Naïve Bayes and Artificial Neural Network to massive volume of healthcare data which, unfortunately, are not “mined” to discover hidden information. For gathering information before processing will helpful in making effective decisions. One Dependency Augmented Naïve Bayes Classifier (ODANB) and Naïve Creedal Classifier 2 (NCC2) are used. This is an extension of Naïve Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. It is difficult to recover discovery of hidden patterns and relationships. Diagnosis of Lung Cancer can answer complex “What if” queries which traditional decision support systems cannot. Using generic lung cancer symptoms such as Age, Sex, Wheezing, Shortness of breath, Pain in shoulder, chest, arm, it can predict the likelihood of patients getting a lung cancer disease. Aim of the paper is to propose a model for early detection and correct diagnosis of the disease which will help the doctor in saving the life of the patient.

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