Accuracy Enhanced Lung Cancer Prognosis for Improving Patient Survivability Using Proposed Gaussian Classifier System

Abstract

Statistical classifier and good accuracy is an essential part of the research in medical data mining. Accurate prediction of lung cancer is an essential step for making effective clinical decisions. After identifying the lung cancer, minimum scopes are available in the medications for patient living in the world. Hemoglobin level and TNM stage wise patients survival period has to be varied. Some group of people survival period is minimal and another group of people survival time is lengthy. This study is aimed to develop a prediction model with new clinical variables to predict lung cancer patients. It's based on revised 8th edition study of TNM in lung cancer. These new attributes are collected from SEER databases, Indian cancer hospitals and research centers. The collected new attributes are classified using supervised machine learning algorithms of linear regression, Naïve Bayes classifier and proposed algorithms of Gaussian K-Base NB classifier. In particular, for TNM stage 1 group of people with normal hemoglobin level (NHBL), that group of lung cancer patient quality of life is highly enhanced. Which proved by using supervised machine learning algorithms. The proposed algorithm classified the database in terms of with respect to tumor size and HB level and the results are confirmed in the R environment. The continuous attribute classification method to prove first level of TNM in lung cancer patient along with standard hemoglobin has to be maintained that the people survivability rate is higher than the smaller level of hemoglobin people survival rate. The Gaussian K-Base NB classifier is more effective than the existing machine learning algorithms for lung cancer prediction model. The proposed classification accuracy has measured using ROC methods.