Year 2016, Volume 2 , Issue 4, Pages 142 - 145 2017-04-20

Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis

David OYEWOLA [1] , Danladi HAKİMİ [2] , Kayode ADEBOYE [3] , Musa Danjuma Shehu [4]


Breast cancer is one of the causes 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  five machine learning which includes: Logistic Regression(LR), Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) and Support  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 receiver operating 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), Random Forest(RF)    methods.  Therefore,  Support Vector Machine (SVM)  learning classification  with  mammography  can  provide  highly  accurate and consistent diagnoses in distinguishing malignant and benign cases for breast cancer predictions.

Logistic Regression, Linear Discriminant Analysis, Random Forest, Quantitative Discriminant Analysis, Support Vector Machine
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Subjects Engineering
Journal Section Articles
Authors

Author: David OYEWOLA
Institution: Federal University of Technology, Minna
Country: Nigeria


Author: Danladi HAKİMİ
Institution: Fatih Sultan Mehmet Üniversitesi
Country: Nigeria


Author: Kayode ADEBOYE
Institution: Federal University of Technology, Minna
Country: Nigeria


Author: Musa Danjuma Shehu

Dates

Publication Date : April 20, 2017

Bibtex @research article { ijet280563, journal = {International Journal of Engineering Technologies IJET}, issn = {2149-0104}, eissn = {2149-5262}, address = {}, publisher = {İstanbul Gelisim University}, year = {2017}, volume = {2}, pages = {142 - 145}, doi = {10.19072/ijet.280563}, title = {Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis}, key = {cite}, author = {Oyewola, David and Haki̇mi̇, Danladi and Adeboye, Kayode and Shehu, Musa Danjuma} }
APA Oyewola, D , Haki̇mi̇, D , Adeboye, K , Shehu, M . (2017). Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis . International Journal of Engineering Technologies IJET , 2 (4) , 142-145 . DOI: 10.19072/ijet.280563
MLA Oyewola, D , Haki̇mi̇, D , Adeboye, K , Shehu, M . "Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis" . International Journal of Engineering Technologies IJET 2 (2017 ): 142-145 <https://dergipark.org.tr/en/pub/ijet/issue/28628/280563>
Chicago Oyewola, D , Haki̇mi̇, D , Adeboye, K , Shehu, M . "Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis". International Journal of Engineering Technologies IJET 2 (2017 ): 142-145
RIS TY - JOUR T1 - Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis AU - David Oyewola , Danladi Haki̇mi̇ , Kayode Adeboye , Musa Danjuma Shehu Y1 - 2017 PY - 2017 N1 - doi: 10.19072/ijet.280563 DO - 10.19072/ijet.280563 T2 - International Journal of Engineering Technologies IJET JF - Journal JO - JOR SP - 142 EP - 145 VL - 2 IS - 4 SN - 2149-0104-2149-5262 M3 - doi: 10.19072/ijet.280563 UR - https://doi.org/10.19072/ijet.280563 Y2 - 2020 ER -
EndNote %0 International Journal of Engineering Technologies Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis %A David Oyewola , Danladi Haki̇mi̇ , Kayode Adeboye , Musa Danjuma Shehu %T Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis %D 2017 %J International Journal of Engineering Technologies IJET %P 2149-0104-2149-5262 %V 2 %N 4 %R doi: 10.19072/ijet.280563 %U 10.19072/ijet.280563
ISNAD Oyewola, David , Haki̇mi̇, Danladi , Adeboye, Kayode , Shehu, Musa Danjuma . "Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis". International Journal of Engineering Technologies IJET 2 / 4 (April 2017): 142-145 . https://doi.org/10.19072/ijet.280563
AMA Oyewola D , Haki̇mi̇ D , Adeboye K , Shehu M . Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis. IJET. 2017; 2(4): 142-145.
Vancouver Oyewola D , Haki̇mi̇ D , Adeboye K , Shehu M . Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis. International Journal of Engineering Technologies IJET. 2017; 2(4): 142-145.