Breast cancer is a significant contributor to female mortality across the world, displaying one of the highest occurrence 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.