aiqbench analysis
This paper applies association rule mining—a standard machine learning technique—to breast cancer classification, focusing on algorithmic improvements like weighted rule generation and pruning rather than novel AI architectures or quantum methods. While the application addresses a critical medical need, the core contribution lies in data mining methodology within the broader field of machine learning.
Classification Based on Association Rules Algorithm for Breast Cancer
arXiv:2610.00174v1 Announce Type: new Abstract: Breast cancer is a significant contributor to female mortality across the world, displaying one of the highest oc currence rates among the various cancer types. In response to the need for early breast cancer detection, researchers have increasingly turned to association rule-based classification as a favored method. Association Rule mining is a data mining approach which offers the benefit of yielding results that are readily understandable for medical professionals. This paper introduces a novel association rule-based data mining technique for breast cancer classification based on a weighted classification approach. This implementation employs three core algorithms: Rule Generation, Rule Pruning, and Rule Prediction. Rule Generation identifies frequent itemsets and creates association rules. Rule Pruning eliminates rules using specific criteria and separates them into major and minor groups based on their influence on training data. Rule Prediction applies the pruned rules to classify test data. The final prediction algorithm was tested on several testing samples to show the feasibility and performance of the approach.
Abstract:Breast cancer is a significant contributor to female mortality across the world, displaying one of the highest oc currence rates among the various cancer types. In response to the need for early breast cancer detection, researchers have increasingly turned to association rule-based classification as a favored method. Association Rule mining is a data mining approach which offers the benefit of yielding results that are readily understandable for medical professionals. This paper introduces a novel association rule-based data mining technique for breast cancer classification based on a weighted classification approach. This implementation employs three core algorithms: Rule Generation, Rule Pruning, and Rule Prediction. Rule Generation identifies frequent itemsets and creates association rules. Rule Pruning eliminates rules using specific criteria and separates them into major and minor groups based on their influence on training data. Rule Prediction applies the pruned rules to classify test data. The final prediction algorithm was tested on several testing samples to show the feasibility and performance of the approach.
Machine Learning (cs.LG)
arXiv:2610.00174 [cs.LG]
(or arXiv:2610.00174v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.00174 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2024 Advances in Science and Engineering Technology International Conferences (ASET), Abu Dhabi, United Arab Emirates, 2024, pp. 1-6
https://doi.org/10.1109/ASET60340.2024.10708711 Focus to learn more DOI(s) linking to related resources
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