Comparative Evaluation of Machine Learning Algorithms in Breast Cancer

Bikhtiyar Friyad Abdulrahman; Roojwan Sc Hawezi; Saja Mohammed Noori M.R; Zhwan mohammed khalid; Shahab Wahhab Kareem; Zanyar Rzgar Ahmed

doi:10.25212/lfu.qzj.7.1.34

Authors

Bikhtiyar Friyad Abdulrahman Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Roojwan Sc Hawezi Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Saja Mohammed Noori M.R Department of Computer Network, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq
Zhwan mohammed khalid Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Shahab Wahhab Kareem Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Zanyar Rzgar Ahmed Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.7.1.34

Keywords:

Machine Learning, Classification algorithms, Neural Network, Breast Cancer, Dataset

Abstract

Breast cancer is one of the world's leading causes of mortality in women and is due to uncontrollable breast cell growth. Early detection and proper care are the only means of avoiding deaths from breast cancer. The precise characterization of tumors is a critical task in the medical profession. Because of their high precision and accuracy, machine learning methods are commonly used in identifying and classifying various forms of cancer. In this review article, the authors have tested different machine learning algorithms and implemented them, which can be used by doctors to identify cancer cells in an early and accurate way. This article introduces several algorithms, including support vector machine (SVM), Nave Bayes Classifier (NBC), artificial neural network (ANN), Random Forest (RF), decision tree (DT), and k-Nearest-Neighbor (KNN). These algorithms are trained with a collection of data containing tumor parameters for a person with breast cancer. After comparing the results, we found the highest accuracy of the Support Vector Machine and Random Forest and the highest precision of the Naive Bayes Classifier (NBC). In addition, we review the number of researches that provide machine learning algorithms for detecting breast cancer

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