چکیده مقاله
Breast cancer remains a significant health concern with rising global incidence, particularly among women Despite advancements in detection and treatment, reducing mortality rates continues to be challenging Early detection is crucial for improving survival rates, especially for small tumors This study proposes a cluster based classification approach using the K means algorithm, Naive Bayes, and Decision Tree classifiers, enhanced by feature selection with a genetic algorithm and meta learning through stacking Evaluated on the Wisconsin Diagnostic Breast Cancer dataset, the framework achieved full accuracy in predictions Future research could explore larger datasets, optimization techniques, and user friendly interfaces for medical professionals
کلیدواژهها
نویسندگان
Alireza Shabani
Department of Industrial Engineering Faculty of Engineering, College of Farabi, University of Tehran Tehran, Iran
Shahrokh Asadi
Department of Industrial Engineering Faculty of Engineering, College of Farabi, University of Tehran Tehran, Iran
شیوه ارجاع
Shabani, Alireza and Asadi, Shahrokh,1403,Optimizing Early Detection of Breast Cancer: A Cluster-Based Classification Approach,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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