Summary of Two New Feature Selection Methods Based on Learn-heuristic Techniques For Breast Cancer Prediction: a Comprehensive Analysis, by Kamyab Karimi et al.
Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis
by Kamyab Karimi, Ali Ghodratnama, Reza Tavakkoli-Moghaddam
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach for improving breast cancer diagnosis is presented in this research. Two novel feature selection methods, based on imperialist competitive algorithm (ICA) and bat algorithm (BA), are introduced and combined with various machine learning algorithms to enhance diagnostic model efficiency. The study focuses on K-nearest neighbors, support vector machine, decision tree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest, logistic regression, and artificial neural network methods. The proposed framework uses wrapper feature selection based on ICA (WFSIC) and BA (WFSB), separately comparing the performance of classifiers. Experimentations are performed on the Wisconsin diagnostic breast cancer dataset, achieving an accuracy of 99.12% with the best diagnostic model using BA. The approach also reduces dataset dimensions by up to 90% and increases the performance of diagnostic models by over 99%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help doctors diagnose breast cancer more accurately is being developed. Machine learning can be used to analyze data and make better decisions. This research presents two new methods for choosing important features in this data, based on algorithms that mimic the behavior of imperialist competitive algorithm (ICA) and bat algorithm (BA). The goal is to improve diagnosis models so doctors can make more precise decisions. The study uses different machine learning techniques like K-nearest neighbors, support vector machine, decision tree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest, logistic regression, and artificial neural network. The results show that the new approach works well, especially when using a random forest classifier. |
Keywords
» Artificial intelligence » Decision tree » Feature selection » Logistic regression » Machine learning » Naive bayes » Neural network » Random forest » Support vector machine
