# Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis

arXiv: https://arxiv.org/abs/2407.14631 · HTML: https://mlsanity.com/paper/2407.14631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [The return of AdaBoost.MH: multi-class Hamming trees](https://mlsanity.com/paper/1312.6086/) (2013)
- [Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial](https://mlsanity.com/paper/2008.13690/) (2020)
- [Bat Algorithm: Literature Review and Applications](https://mlsanity.com/paper/1308.3900/) (2013)
- [Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System](https://mlsanity.com/paper/1910.13574/) (2019)
- [An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants](https://mlsanity.com/paper/2109.14441/) (2021)

## Abstract (arXiv)

Breast cancer is not preventable because of its unknown causes. However, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make much more precise and reliable decisions than before. K-nearest neighbors, support vector machine, decision tree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest, logistic regression, and artificial neural network are some of the methods employed. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12%, surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90% and increasing the performance of diagnostic models by over 99%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models.