Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms
Joint Authors
Dhahri, Habib
Mahmood, Awais
Al Maghayreh, Eslam
Elkilani, Wail
Faisal, Mohammed
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques.
Many claim that their algorithms are faster, easier, or more accurate than others are.
This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors.
The aim of this study was to optimize the learning algorithm.
In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers.
The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves.
The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.
American Psychological Association (APA)
Dhahri, Habib& Al Maghayreh, Eslam& Mahmood, Awais& Elkilani, Wail& Faisal, Mohammed. 2019. Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175203
Modern Language Association (MLA)
Dhahri, Habib…[et al.]. Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms. Journal of Healthcare Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1175203
American Medical Association (AMA)
Dhahri, Habib& Al Maghayreh, Eslam& Mahmood, Awais& Elkilani, Wail& Faisal, Mohammed. Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175203
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175203