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.

Welcome to the February edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted among others, articles about AI that can diagnose breast cancer with higher accuracy than experts! Let's start, as usual, with the comic of the month: A recent evaluation of a AI system for breast cancer screening concludes that it is capable of surpassing human experts in breast cancer prediction. It is essential to identify breast cancer at earlier stages of the disease when treatment can be more successful.

Artificial intelligence has transformed our world drastically. And this fact is more than evident by just taking a look around. Every device or technology that is surrounding us is becoming in ways powered by artificial intelligence. Be it our smartphone, mobile applications, software, websites, smartwatches or IT services like healthcare. Artificial intelligence is penetrating every other industry and impacting it in several ways.