Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms


  • Hezha M.Tareq Abdulhadi Department of Information Technology, National Institute of Technology (NIT), Sulaymaniyah, KRG, Iraq
  • Hardi Sabah Talabani Department of Applied Computer, College of Medical and Applied Sciences, Charmo University, Sulaymaniyah, KRG, Iraq



Ranker feature selection, Information gain, Gain Ratio, supervised Machine learning Algorithms, Thoracic Surgery, Cross-Validation.


Thoracic surgery refers to the information gathered for the patients who have to suffer from lung cancer. Various machine learning techniques were employed in post-operative life expectancy to predict lung cancer patients. In this study, we have used the most famous and influential supervised machine learning algorithms, which are J48, Naïve Bayes, Multilayer Perceptron, and Random Forest (RF). Then, two ranker feature selections, information gain and gain ratio, were used on the thoracic surgery dataset to examine and explore the effect of used ranker feature selections on the machine learning classifiers. The dataset was collected from the Wroclaw University in UCI repository website. We have done two experiments to show the performances of the supervised classifiers on the dataset with and without employing the ranker feature selection. The obtained results with the ranker feature selections showed that J48, NB, and MLP’s accuracy improved, whereas RF accuracy decreased and support vector machine remained stable.


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