A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease. (March 2023) - Kumari, Nancy; Acharjya, D.P.
Abstract: Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding. Besides, such data also contain many extraneous features that do not influence the foreboding accuracy while diagnosing a disease. The data must eliminate these extraneous features to get a better diagnosis. Ultimately, the minimized information system will lead to a better diagnosis. In this paper, we have introduced an incremental rough set shuffled frog leaping algorithm for knowledge inference. The proposed algorithm helps find minimum features from an information system while handling complex databases with uncertainty and incompleteness. The proposed rough set shuffled frog leaping knowledge inference model works in two phases. In the initial phase, the incremental rough set shuffled frog leaping algorithm is used to get the most relevant features. Identifying the relevant features is carried out using a fitness function, which uses the rough degree of dependency. The use of the fitness function identifies the much information with the minimum number of features. The purpose of feature selection is to identify a feature subset from an original set of features without reducing the predictive accuracy and to scale back the computation overhead in the data processing. In the second phase, a rough set is utilized for knowledge discovery in perception with rule generation. The selection of decision rules is carried out based on the accuracy of the decision rule and a predefined threshold value. An empirical analysis of the lung disease information system and a comparative study is conducted. Experimental outcomes exhibit that hybrid techniques express the feasibility of the proposed model while achieving better classification accuracy. Highlights: An innovative information retrieval system hybridizing SFL and RS techniques is proposed. It, in turn, helps physicians to think of alternative decisions. The proposed SFLRS model is analyzed over the lung cancer decision system, and decision rules are generated for the decision support system. A comparison of the hybridized model SFLRS with DT, RS, and SFLDT concerning accuracy is carried out. The proposed model SFLRS achieves high accuracy of 93.19% as compared to other models. The statistical tests concerning the Friedman test, Wilcoxon signed-rank test, and Mann–Whitney U test is carried out to check the consistency of data. The hybridized model, SFLRS, produces 26.47% fewer decision rules in comparison to the traditional RS model with 11 features.