About this document

Data Mining in Lung Cancer Surgery by Samuel Senbetu is a document available to read on EtoBox.

This study evaluates the applicability of the Cross-Industry Standard Process for Data Mining (CRISP-DM) in the lung cancer surgery domain, demonstrating its effectiveness in enhancing decision-making and quality management. A data mining project analyzed records from 501 patients, employing logistic regression to identify risk factors for perioperative mortality. The findings indicate that CRISP-DM is a valuable methodology for improving surgical outcomes through data-driven insights.

Author

Samuel Senbetu

Language

EN