| Title: |
Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis |
| Authors: |
Oyewola, David; Hakimi, Danladi; Adeboye, Kayode; Shehu, Musa Danjuma |
| Source: |
Volume: 2, Issue: 4142-145 ; 2149-0104 ; 2149-5262 ; International Journal of Engineering Technologies ; International Journal of Engineering Technologies IJET |
| Publisher Information: |
İstanbul Gelişim Üniversitesi; İstanbul Gelisim University |
| Publication Year: |
2016 |
| Collection: |
DergiPark Akademik (E-Journals) |
| Subject Terms: |
Logistic Regression; Linear Discriminant Analysis; Random Forest; Quantitative Discriminant Analysis; Support Vector Machine; Engineering; Mühendislik |
| Description: |
Breast cancer is one of thecauses of female death in the world. Mammography is commonly used for distinguishing malignant tumors from benign ones. In this research, a mammographic diagnostic method is presented for breast cancer biopsy outcome predictions using fivemachine learning which includes: Logistic Regression(LR), Linear DiscriminantAnalysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) andSupport Vector Machine(SVM) classification. The testing results showed that SVM learning classification performs better than other with accuracy of 95.8% in diagnosing both malignant and benign breast cancer, a sensitivity of 98.4% in diagnosing disease, a specificity of 94.4%. Furthermore, an estimated area of the receiveroperating characteristic (ROC) curve analysis for Support vector machine (SVM) was 99.9% for breast cancer outcome predictions, outperformed the diagnostic accuracies of Logistic Regression(LR),Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), RandomForest(RF) methods. Therefore, Support Vector Machine(SVM) learning classification with mammography can provide highly accurate and consistentdiagnoses in distinguishing malignant and benign cases for breast cancerpredictions. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://dergipark.org.tr/tr/download/article-file/295451; https://dergipark.org.tr/tr/pub/ijet/issue/28628/280563 |
| DOI: |
10.19072/ijet.280563 |
| Availability: |
https://dergipark.org.tr/tr/pub/ijet/issue/28628/280563; https://doi.org/10.19072/ijet.280563 |
| Accession Number: |
edsbas.F1297261 |
| Database: |
BASE |