scholarly journals Domain expertise–agnostic feature selection for the analysis of breast cancer data*

2020 ◽  
Vol 108 ◽  
pp. 101928 ◽  
Author(s):  
Susanna Pozzoli ◽  
Amira Soliman ◽  
Leila Bahri ◽  
Rui Mamede Branca ◽  
Sarunas Girdzijauskas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Domain Expertise ◽  
Selection For

scholarly journals Research of Feature Selection Methods to Predict Breast Cancer

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2353-2355 ◽  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Early Stage ◽  
Classification Methods ◽  
Selection Methods ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Cancer Breast ◽  
Data Mining Approach ◽  
The World

Human health is most important than anything in the world, one should take care of it. Among various disease, cancer is the most terrible and deadly disease, so it is necessary to predict such disease in early stage. In this paper different feature selection methods used for feature extraction with different feature classification methods to identify the breast cancer. Breast cancer data is taken from UCI repository and is processed using WEKA tool and proposed techniques are applied to classify data accurately. This study well defines that data mining approach is suitable for predicting breast cancer.

scholarly journals Ensemble Feature Selection for Breast Cancer Classification using Microarray Data

2020 ◽  
Vol 23 (65) ◽  
pp. 100-114
Author(s):  
Supoj Hengpraprohm ◽  
Suwimol Jungjit
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cross Validation ◽  
Microarray Dataset ◽  
Classification Model ◽  
Cancer Data ◽  
Evaluation Functions ◽  
Selection For ◽  
Feature Selection Approach ◽  
Fold Cross Validation

For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed ‘EnSNR’ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelevant features (genes), there is also a small saving of computer processing time.

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