Improving Breast Cancer Risk Stratification Using Resonance-Frequency Electrical Impedance Spectroscopy Through Fusion of Multiple Classifiers

2010 ◽  
Vol 39 (3) ◽  
pp. 931-945 ◽  
Author(s):  
Dror Lederman ◽  
Bin Zheng ◽  
Xingwei Wang ◽  
Xiao Hui Wang ◽  
David Gur
Keyword(s):  
Breast Cancer ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Resonance Frequency ◽  
Electrical Impedance ◽  
Electrical Impedance Spectroscopy ◽  
Multiple Classifiers

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
Keyword(s):  
Breast Cancer ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Resonance Frequency ◽  
Electrical Impedance ◽  
Electrical Impedance Spectroscopy

scholarly journals Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Md. Toukir Ahmed ◽  
Md. Rayhanul Masud ◽  
Abdullah Al Mamun
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Electrical Impedance ◽  
Performance Metrics ◽  
Electrical Impedance Spectroscopy

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.

Electrical impedance scanning as a new breast cancer risk stratification tool for young women

2008 ◽  
Vol 97 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Alexander Stojadinovic ◽  
Aviram Nissan ◽  
Craig D. Shriver ◽  
Elizabeth A. Mittendorf ◽  
Mark D. Akin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Young Women ◽  
Electrical Impedance ◽  
Risk Stratification Tool ◽  
Electrical Impedance Scanning

scholarly journals Health professionals’ perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease

2019 ◽  
Vol 40 (1) ◽  
Author(s):  
Svetlana Puzhko ◽  
Justin Gagnon ◽  
Jacques Simard ◽  
Bartha Maria Knoppers ◽  
Sophia Siedlikowski ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Health Professionals

scholarly journals Uptake of Risk Appropriate Behaviors After Breast Cancer Risk Stratification in the Mammography Screening Population

2020 ◽  
Vol 17 (10) ◽  
pp. 1285-1288
Author(s):  
Claire C. Conley ◽  
Bethany L. Niell ◽  
Bianca M. Augusto ◽  
McKenzie McIntyre ◽  
Richard Roetzheim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Mammography Screening ◽  
Screening Population
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