Role of Rough Neutrosophic Attribute Reduction with Deep Learning-Based Enhanced Kidney Disease Diagnosis

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The kidneys have an important role in keeping blood pressure, electrolyte sense, and acid-base sense of body balance to remove toxins from our body. Malfunction is responsible for irrelevant to life-threatening diseases, along with malfunction in the other functional organs. As a result, scholars worldwide have committed to finding methods for effectively diagnosing and accurately treating chronic kidney disease. As machine learning (ML) classifier is widely deployed in the healthcare field for diagnoses, also CKD is now involved in the collection of disorders that could be predicted through the ML classifier. Neutrosophic logic (NL) can be employed as a form of logic that expands classical, fuzzy, and intuitionistic fuzzy logic (IF) by integrating a third constituent: indeterminacy. It enables data handling and representation with three dissimilar membership functions: truth (T), indeterminacy (I), and false (F). The complete set is independent and may differ in the interval [0, 1], providing a convoluted strategy to handle, data incompleteness, vagueness and uncertainty. This makes NL especially relevant in complicated systems where data might be partially unknown, ambiguous, or inconsistent. This article employs a Rough Neutrosophic Attribute Reduction with Deep Learning based Enhanced Kidney Disease Diagnosis (RNSAR-DLKDD) technique. Initially, the RNSAR-DLKDD technique reduces the attributes via the RNSAR technique. Followed by, the detection and classification of kidney disease take place using long short-term memory (LSTM) model. Finally, the hyperparameter selection process is carried out via crow search algorithm (CSA). To highlight the performance of the RNSAR-DLKDD technique, a series of experiments were involved. The extensive results inferred the betterment of the RNSAR-DLKDD technique over other models