scholarly journals A GMM-based breast cancer risk stratification using a resonance-frequency electrical impedance spectroscopy

2011 ◽  
Vol 38 (3) ◽  
pp. 1649-1659 ◽  
Author(s):  
Dror Lederman ◽  
Bin Zheng ◽  
Xingwei Wang ◽  
Jules H. Sumkin ◽  
David Gur
Author(s):  
Md. Toukir Ahmed ◽  
Md. Rayhanul Masud ◽  
Abdullah Al Mamun

Nowadays, women worldwide are affected greatly by Breast cancer. The consequences of the disease and the number of affected are so alarming that it requires immediate attention. Prediction of the disease is the primary and most significant route to prevention of it. This study aims to have a comparison among multiple machine learning based classifiers for breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy. Five machine learning based classifiers namely- Naïve Bayes, Multilayer perceptron, J48, Bagging and Random Forest were applied to the dataset and a comparison was made based on different performance metrics. The study demonstrated that Random Forest classifier performed slightly better than the others in both splitting and folding of the dataset.


2008 ◽  
Vol 97 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Alexander Stojadinovic ◽  
Aviram Nissan ◽  
Craig D. Shriver ◽  
Elizabeth A. Mittendorf ◽  
Mark D. Akin ◽  
...  

2019 ◽  
Vol 40 (1) ◽  
Author(s):  
Svetlana Puzhko ◽  
Justin Gagnon ◽  
Jacques Simard ◽  
Bartha Maria Knoppers ◽  
Sophia Siedlikowski ◽  
...  

2020 ◽  
Vol 17 (10) ◽  
pp. 1285-1288
Author(s):  
Claire C. Conley ◽  
Bethany L. Niell ◽  
Bianca M. Augusto ◽  
McKenzie McIntyre ◽  
Richard Roetzheim ◽  
...  

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