Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

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 const...

Full description

Saved in:
Bibliographic Details
Published in:Journal of healthcare engineering 2019, Vol.2019 (2019), p.1-11
Main Authors: Elkilani, Wail, Mahmood, Awais, Al Maghayreh, Eslam, Dhahri, Habib, Faisal, Mohammed
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Cancer
Data mining
Diagnosis
Machine learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2040-2295
2040-2309
DOI:10.1155/2019/4253641