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

Year 2016, Volume: 2 Issue: 4, 142 - 145, 20.04.2017

David Oyewola Danladi Hakimi Kayode Adeboye Musa Danjuma Shehu

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

Cited By: 5

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

References

• Department of Health and Human Services Centers for Disease Control and Prevention, World Cancer Day, February 3, 2015.
• Department of Health and Human Services Centers for Disease Control and Prevention, United States Cancer Statistics, Technical Notes 2007.
• American Cancer Society, Cancer Facts & Figures 2016, Atlanta, Georgia, American Cancer Society, pp. 1– 63, 2016.
• Simes RJ. Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. J Chronic Dis, 38:171-86, 1985.
• Maclin PS, Dempsey J, Brooks J, et al. Using neural networks to diagnose cancer J Med Syst, 15:11-9, 1991.
• Cicchetti DV. Neural networks and diagnosis in the clinical laboratory: state of the art. Clin Chem, 38:9-10, 1992.
• Petricoin EF, Liotta LA. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Curr Opin Biotechnol, 15:24-30, 2004.
• Bocchi L, Coppini G, Nori J, Valli G. Detection of single and clustered micro calcifications in mammograms using fractals models and neural networks. Med Eng Phys, 26:303-12, 2004.
• Zhou X, Liu KY, Wong ST. Cancer classification and prediction using logistic regression with Bayesian gene selection. J Biomed Inform, 37:249-59, 2004.
• Dettling M. Bag Boosting for tumor classification with gene expression data. Bioinformatics, 20:3583-93, 2004.
• Wang JX, Zhang B, Yu JK, et al. Application of serum protein finger printing coupled with artificial neural network model in diagnosis of hepatocellular carcinoma. Chin Med J (Engl), 118:1278-84, 2005.
• McCarthy JF, Marx KA, Hoffman PE, et al. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci, 1020:239-62, 2004.
• L. Breiman. Bagging predictors. Machine Learning, 24:123–140, 1996.
• P. Buhlmann and B. Yu. Analyzing bagging, The Annals of Statistics, 30:927–961, 2002.
• A. Buja and W. Stuetzle. Observations on bagging. Statistica Sinica, 16:323–352, 2006.
• G. Biau, F. Cerou, and A. Guyader. On the rate of convergence of the bagged nearest neighbor estimate. Journal of Machine Learning Research, 11:687–712, 2010.