| An Enhanced Prediction of Expected Lifespan Following Thoracic Surgery |
| Paper ID : 1065-ISCBAS |
| Authors |
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Lamiaa M. El Bakrawy1, Abeer S. Desuky *2 1Faculty of Science, Al-Azhar University 2Prof. of Computer Science, Al-Azhar University |
| Abstract |
| Enhancing quality initiatives, healthcare management, and consumer education requires monitoring health outcomes. Thoracic Surgery involves collecting data on patients who have undergone major lung resections for primary lung cancer. Despite limited research and recommendations, machine learning techniques can be applied to predict an expected lifespan following Thoracic Surgery. To effectively use these techniques, attribute ranking, and selection are crucial for successful health outcome prediction. This paper presents three attribute ranking and selection methods to enhance algorithm performance in health outcomes research. We compare the efficiency of our proposed methods with two other papers' results to demonstrate their effectiveness and accuracy. |
| Keywords |
| Machine learning, Attribute ranking, Attribute selection, Healthcare Management, Thoracic Surgery. |
| Status: Abstract Accepted (Oral Presentation) |