A Fuzzy Rule-Based System for Classification of Diabetes

Abstract

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

“…Feature selection is then proceeded by the classification process which classifies the face image in terms of age and gender. Age is categorized into 8 classes as (0-2), (4-6), (8)(9)(10)(11)(12), (15)(16)(17)(18)(19)(20), (25)(26)(27)(28)(29)(30)(31)(32), (38)(39)(40)(41)(42)(43), (48)(49)(50)(51)(52)(53), and (60-100), and gender is categorized as male/female. Figure 7 shows the result of classification.…”

“…DBN learning is quicker than DNN due to the inclusion of RBM. The RBMs are stacked DBM with unguided connections across the levels [48][49][50][51][52][53].…”

Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning

“…Hybrid fuzzy systems are used in the past for understanding customer behavior [80], heart disease diagnosis [81], and diabetes prediction [81]. Fuzzy logic classifier along with reinforcement learning is used for the development of an intelligent power transformer [82], for handling continuous inputs and learning from continuous actions [83], for small lung nodules detection [84], for finding appropriate pedagogical content [85], for robotic soccer games [44,45], for water blasting system for ship hull corrosion cleaning [86], and for classification of diabetes [73]. All the above mentioned have used fuzzy systems to figure out the issues and solutions in different concepts, but none of them has prioritized the problems or solutions identified in the different concepts.…”

“…A fuzzy set S is represented as {E, µ S (x) | x e X} where X {x 1 , x 2 , x 3 …} and µ S (x) [73] For a TFN (u, v, w), its membership function is defined in Eq. ( 1)…”

FMEA in Smartphones: A Fuzzy Approach

“…Fuzzy logic has been reflected in several domains like automobile speed control [10], control of robotic manipulators [11], water filter automation [12], and operating systems of automatic trains [13]. Aamir et al proposed a fuzzy rule-based model for the classification of diabetics, and their accurate results indicated that the logic could be further utilized in the healthcare sector [14]. Furthermore, a comprehensive review by Mittal et al has highlighted the importance and potential of fuzzy logic in hardware implementations, medical diagnosis, big data, and robotics applications [15].…”