Improved Prediction of Post-operative Life Expectancy after Thoracic Surgery

Abstract

Monitoring health outcomes is essential to enhance quality initiatives, healthcare management and consumer education. Thoracic Surgery is the data collected for patients who underwent major lung resections for primary lung cancer. The application of machine learning techniques for predicting post-operative life expectancy in the lung cancer patients is an area with little research and few concrete recommendations. In order to use machine learning techniques effectively, attribute ranking and selection is an integral component to successful health outcome prediction. In this paper, we present three attribute ranking and selection methods to improve algorithms performance for health outcomes research. Two papers results for other researchers are used in comparison to show the efficiency of our proposed attribute ranking and selection methods.