Feature Selection Techniques on Thyroid, Hepatitis, and Breast Cancer Datasets

2013 ◽  
Vol 3 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad Ashraf ◽  
Girija Chetty ◽  
Dat Tran
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Feature Selection Techniques

scholarly journals Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22090-22105
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Abdus Saboor ◽  
Jalaluddin Khan ◽  
Samad Wali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Clinical Data ◽  
Unsupervised Feature Selection ◽  
Feature Selection Techniques

scholarly journals Augmentation of Classifier Accuracy through Implication of Feature Selection for Breast Cancer Prediction

2019 ◽  
Vol 8 (2) ◽  
pp. 6396-6399
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Multilayer Perceptrons ◽  
Accuracy Rate ◽  
Cancer Prediction ◽  
Malignant Breast ◽  
Selection For ◽  
Breast Lumps ◽  
Feature Selection Techniques

Breast Cancer Examination and Prediction are great provocations to the researchers in the medical applications. Breast Cancer Examination distinguishes benign from malignant breast lumps, Breast Cancer Prediction has great deal in foretelling when Breast Cancer is expected to reoccur in patients that have had their cancers excised. Feature Selection is considered to be the preliminary step used in process to find best subsets of attributes. In this paper authors confer about the performance of five classifiers Sequential minimal optimization (SMO), Multilayer Perceptrons, Kstar, Decision Table and Random Forest with and without feature selection. The results manifest that after implying two feature selection techniques such as Correlation based and information based with ranker algorithm there is an augmentation in the accuracy rate of the classifier. It has been observed that after through implication feature selection techniques accuracy of the classifiers such as SMO, Multilayer Perceptrons, Kstar, Decision Trees, and Random Forest are enhanced.

Predictive Modeling for Classification of Breast Cancer Dataset Using Feature Selection Techniques

2021 ◽  
pp. 204-215
Author(s):  
Leena Nesamani S. ◽  
S. Nirmala Sigirtha Rajini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Mutual Information ◽  
Predictive Modeling ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Chi Squared ◽  
Feature Selection Techniques

Predictive modeling or predict analysis is the process of trying to predict the outcome from data using machine learning models. The quality of the output predominantly depends on the quality of the data that is provided to the model. The process of selecting the best choice of input to a machine learning model depends on a variety of criteria and is referred to as feature engineering. The work is conducted to classify the breast cancer patients into either the recurrence or non-recurrence category. A categorical breast cancer dataset is used in this work from which the best set of features is selected to make accurate predictions. Two feature selection techniques, namely the chi-squared technique and the mutual information technique, have been used. The selected features were then used by the logistic regression model to make the final prediction. It was identified that the mutual information technique proved to be more efficient and produced higher accuracy in the predictions.

scholarly journals A Review on Feature Selection Techniques in Digital Mammograms

2021 ◽  
Vol 12 (2) ◽  
pp. 3329-3338
Author(s):  
L Kanya kumara, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Accurate Method ◽  
Optimization Techniques ◽  
Computer Aided Detection ◽  
Resonance Imaging ◽  
Cad Systems ◽  
The World ◽  
Feature Selection Techniques ◽  
Low Dosage

The most of the women in the world are suffering from a deadly disease called Breast Cancer (BC). Breast cancer is analyzed by using imaging modalities such as mammograms, magnetic resonance imaging, ultrasound, and thermograms. Among all, mammograms are the low dosage, less cost, more effective, and accurate method to detect BC in early stages. There are many Computer-Aided Detection (CAD) systems for the automatic detection of masses in mammograms. These techniques are helping radiologists and physicians in diagnosing disease. The objective of this paper is to overview different CAD systems in which mainly we focused on feature selection, as feature selection techniques are used to reduce the complexity of the classifiers and also increase the accuracy. We conclude that suitable optimization techniques should be chosen to increase the accuracy of the classifier so that we can increase the survival rate of the patient.

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