Tracking health outcomes is essential for enhancing quality initiatives, healthcare management, and consumer education. Thoracic surgery refers to the collection of information from patients who underwent extensive lung resections for primary lung cancer. When utilising machine learning algorithms to predict health outcomes, attribute ranking and selection are essential elements. Before symptoms occurred, researchers employed a variety of techniques, such as early-stage examinations, to identify the type of cancer. Utilizing attribute ranking and selection, the most pertinent attributes are found, and the redundant and extraneous attributes are eliminated from the dataset. Many machine learning models like SVM, naïve Bayes, decision tree, random forest, logistic regression have been applied for post thoracic surgery life expectancy prediction based on data set.