基于关联规则算法的乳腺癌分类
Classification Based on Association Rules Algorithm for Breast Cancer
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中文总结 AI 辅助
本文提出一种基于加权分类的关联规则数据挖掘方法用于乳腺癌分类，通过规则生成、剪枝和预测三个核心算法实现，并在测试样本上验证了其可行性和性能。
中文摘要 AI 辅助
乳腺癌是全球女性死亡的重要因素，在各种癌症类型中发病率最高之一。为了满足早期乳腺癌检测的需求，研究人员越来越多地转向基于关联规则的分类作为一种受欢迎的方法。关联规则挖掘是一种数据挖掘方法，其优点是能够产生医学专业人员易于理解的结果。本文介绍了一种新颖的基于关联规则的乳腺癌分类数据挖掘技术，该方法基于加权分类方法。该实现采用了三个核心算法：规则生成、规则剪枝和规则预测。规则生成识别频繁项集并创建关联规则。规则剪枝使用特定标准消除规则，并根据其对训练数据的影响将其分为主要组和次要组。规则预测应用剪枝后的规则对测试数据进行分类。最终的预测算法在多个测试样本上进行了测试，以展示该方法的可行性和性能。
英文摘要
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.
发表机构
- University of Sharjah（沙迦大学）
机构由 AI 辅助整理，请以论文原文为准。
