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

Year 2016, , 142 - 145, 20.04.2017

David Oyewola , Danladi Hakimi Kayode Adeboye Musa Danjuma Shehu

https://doi.org/10.19072/ijet.280563

Cited By: 6

Abstract

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.

Keywords

Logistic Regression, Linear Discriminant Analysis, Random Forest, Quantitative Discriminant Analysis, Support Vector Machine

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