Size reduction mechanism in real life data sets are very important and an essential factor in healthcare based machine learning (ML) analysis due to high dimension in nature. ML based feature selection aims in determining a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough Set (RS) theory provides the mechanism of discovery of data dependencies in the data set and the novel reducts facilitates, the reduction of the number of conditional attributes and the set of associated objects contained in a dataset in preserving the information of the original dataset. The process use the data alone and does not need any additional information. This paper presents the fundamental concepts of RS and Tolerance RS approaches and adapts the related feature selection for two relevant healthcare applications. Firstly, the TRS based feature selection method is used in latest developments of three medical dataset classification analysis, secondly the method is used in Chest X-Ray image analysis for nCOVID19 diagnose or test classifications and non-invasive thermal imaging process to detect inflammation and vascular dysfunction for sensitive screening of nCOVID19 cases.
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https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
https://archive.ics.uci.edu/ml/datasets/parkinsons
https://www.kaggle.com/uciml/pima-indians-diabetes-database
https://github.com/harpreetSinghGuller/Data-Science-R/blob/master/SAHeart.csv
https://github.com/ieee8023/COVID-chestxray-dataset
Rough set, Tolerance Rough Set, Feature Selection, Reducts, Chest X-Ray image analysis, Thermal Imaging classification.
