Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

2019 ◽  
pp. 18-30 ◽  
Author(s):  
Jesús Silva ◽  
Omar Bonerge Pineda Lezama ◽  
Noel Varela ◽  
Luz Adriana Borrero
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Ensemble Learning ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Classification Techniques

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence

2017 ◽  
Vol 78 ◽  
pp. 47-54 ◽  
Author(s):  
Chih-Jen Tseng ◽  
Chi-Jie Lu ◽  
Chi-Chang Chang ◽  
Gin-Den Chen ◽  
Chalong Cheewakriangkrai
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Ovarian Cancer ◽  
Ensemble Learning ◽  
Cancer Recurrence ◽  
Classification Techniques

scholarly journals Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

2020 ◽  
Vol 6 (3) ◽  
pp. 313
Author(s):  
Zuhaira Muhammad Zain ◽  
Mona Alshenaifi ◽  
Abeer Aljaloud ◽  
Tamadhur Albednah ◽  
Reham Alghanim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Medical Information ◽  
Cancer Recurrence ◽  
Principal Component ◽  
Component Analysis ◽  
Breast Cancer Recurrence ◽  
F Measure

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.

scholarly journals Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

2020 ◽  
pp. 584-596
Author(s):  
Morales-Ortega Roberto Cesar ◽  
Lozano-Bernal German ◽  
Ariza-Colpas Paola Patricia ◽  
Arrieta-Rodriguez Eugenia ◽  
Ospino-Mendoza Elisa Clementina ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Recurrence Analysis ◽  
Data Mining Techniques

Ensemble Learning Method for the Prediction of Breast Cancer Recurrence

2018 ◽  
Author(s):  
Daad Abdullah Almuhaidib ◽  
Fatima Moteb Albusayyis ◽  
Hadil Ahmed Shaiba ◽  
Mashael Abdulalim Alzaid ◽  
Najla Ghazi Alharbi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ensemble Learning ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Learning Method
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