A Comparative Analysis of Feature Selection Methods and Associated Machine Learning Algorithms on Wisconsin Breast Cancer Dataset (WBCD)

2016 ◽  
pp. 215-224 ◽  
Author(s):  
Nileshkumar Modi ◽  
Kaushar Ghanchi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Comparative Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Breast Cancer Dataset ◽  
Selection Methods ◽  
Cancer Dataset

Breast Cancer Prediction and Classification Using Supervised Learning Techniques

2020 ◽  
Vol 17 (6) ◽  
pp. 2519-2522
Author(s):  
Kalpna Guleria ◽  
Avinash Sharma ◽  
Umesh Kumar Lilhore ◽  
Devendra Prasad
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Breast Cancer Dataset ◽  
Cancer Awareness ◽  
Cancer Dataset ◽  
Cancer Prediction

Approximately 2.1 million women every year are affected due to breast cancer which has become one of the major causes for cancer related deaths among women. World Health Organization’s (WHO) report 2018, reveals that around 15% of deaths among women are due to breast cancer. Lack of awareness is one of the major reason which has led to the detection of breast cancer at the later stage. Another major reason is access to limited health resources which make the problem worse. Early or timely detection of breast cancer is utmost important to increase the survival rate of the patients. World Health Organization’s (WHO) cancer awareness guidelines recommend that women aged between 40–49 years of age or 70–75 years of age must be subjected to mammographic screening which will provide the timely detection of the problem, if it persist. This article uses Breast Cancer dataset from UCI machine learning repository to predict and diagnose the class of breast cancer: benign or malignant by using supervised learning. Supervised machine learning algorithms: KNearest Neighbor (K-NN), Naive Bayes, logistic regression and decision tree have been utilized for breast cancer prediction. The performance evaluation of these classification algorithms is done based on various performance measures: accuracy, sensitivity, specificity and F -measure.

Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods

2018 ◽  
Vol 14 (1) ◽  
pp. 64-73 ◽  
Author(s):  
ShaoPeng Wang ◽  
Deling Wang ◽  
JiaRui Li ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Signal Peptide ◽  
Cleavage Site ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cleavage Sites ◽  
Selection Methods

Several machine learning algorithms were adopted to investigate cleavage sites in a signal peptide. An optimal dagging based classifier was constructed and 870 important features were deemed to be important for this classifier.

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