scholarly journals Breast Cancer Risk Diagnosis based on Random Forest Classification

2016 ◽  
Author(s):  
Li Li ◽  
Yuting Sun ◽  
Lei Xiao
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Random Forest Classification ◽  
Forest Classification

Feature Selection and Random Forest Classification for Breast Cancer Disease

2021 ◽  
pp. 191-210
Author(s):  
Shubham Raj ◽  
Swati Singh ◽  
Avinash Kumar ◽  
Sobhangi Sarkar ◽  
Chittaranjan Pradhan
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Disease ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Breast Cancer Risk Prediction in African Women using Random Forest Classifier

2021 ◽  
pp. 100396
Author(s):  
Babafemi Oluropo MACAULAY ◽  
Benjamin Segun ARIBISALA ◽  
Soji Alabi AKANDE ◽  
Boluwaji Ade AKINNUWESI ◽  
Olusola Aanu OLABANJO
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Random Forest Classifier ◽  
African Women

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.

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