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    Covering-based rough set classification system
    (Springer London Ltd, 2017) Kumar, S. Senthil; Inbarani, H. Hannah; Azar, Ahmad Taher; Polat, Kemal
    Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.
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    Retraction Note: Covering-based rough set classification system (Neural Computing and Applications, (2017), 28, 10, (2879-2888), 10.1007/s00521-016-2412-7)
    (Springer Science and Business Media Deutschland GmbH, 2024) Kumar, S. Senthil; Inbarani, H. Hannah; Azar, Ahmad Taher; Polat, Kemal
    The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
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    Tolerance rough set firefly-based quick reduct
    (Springer London Ltd, 2017) Ganesan, Jothi; Inbarani, Hannah H.; Azar, Ahmad Taher; Polat, Kemal
    In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.