Design and Development a Hybrid Classifier to Improve Lung Cancer Diagnosis.

Abstract

Lung cancer is one of the dangerous diseases that cause massive cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient's chance for survival; the early detection of cancer can help cure the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in the early stage is increasing. Earlier diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing sudden fatal end. Data mining is a powerful technique to help people in their health, Scientific, and Engineering. It uses a learning method to understand the data patterns. Those techniques are extracting the secret information from the large databases, which helps to find the relationships and patterns from the data. The hybrid classifier is used for classification of lung cancer dataset as it gives much higher accuracy than other classifiers.. These evaluations and results are carried out using WEKA. 3.6.10 as a data mining tool.

 Keywords: Lung Cancer. Data Mining, Classification, Weka, SMOTE