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. 2019 Nov 3:2019:4253641.
doi: 10.1155/2019/4253641. eCollection 2019.

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

Habib Dhahri  1   2 , Eslam Al Maghayreh  1   3 , Awais Mahmood  1 , Wail Elkilani  1 , Mohammed Faisal Nagi  1
Affiliations

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

Habib Dhahri et al. J Healthc Eng. .

Abstract

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.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Example of pipeline.
Figure 2
Figure 2
Flowchart of GP.
Figure 3
Figure 3
Wrapper methods.
Figure 4
Figure 4
Embedded methods.
Figure 5
Figure 5
ROC curve for LDA.
Figure 6
Figure 6
ROC curve for LR.
Figure 7
Figure 7
ROC curve for ET.
Figure 8
Figure 8
ROC curve for RF.
Figure 9
Figure 9
ROC curve for GB.
Figure 10
Figure 10
ROC curve for AB.
Figure 11
Figure 11
ROC curve for DT.
Figure 12
Figure 12
ROC curve for KNN.
Figure 13
Figure 13
ROC curve for GNB.
Figure 14
Figure 14
Combining feature extraction.
Figure 15
Figure 15
Comparison of classifier accuracy.
Figure 16
Figure 16
Comparison of log-loss classifier.
Figure 17
Figure 17
Validation accuracy.
See this image and copyright information in PMC

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