A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis

By Huiling Chen, Bo Yang, Jie Liu, Da-you Liu. Expert systems with applications, 2011

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A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms (2018, methodology): “0e positive semidefinite functions obey Mercer’s condition as kernel functions [32].”
SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier (2014, background): “Its scope of application is widely used in various fields, such as disease or medical imaging diagnosis [3–5], financial crisis prediction [6], biomedical engineering, and bioinformatics classification [7, 8].”
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis (2014, methodology): “The main advantage of RS theory is that it does not need any preliminary or additional information about data: like probability in statistics or basic probability assignment in Dempster – Shafer theory and membership grade in fuzzy set theory [22].”