Intelligent Neutrosophic Diagnostic System for Cardiotocography Data

Abstract

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the other algorithms. The experimental results visualize the data using the boxplot for better understanding of attribute distribution. The performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. The comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image.

[Retracted] A Decision‐Making Framework for University Student Sports Study Psychological Healthy Evaluation with 2‐Tuple Linguistic Neutrosophic Numbers

“…Compared with IFSs, the NSs add an independent uncertainty measure, which is an extension and generalization of fuzzy sets and IFSs. Since the theory of NSs was put forward, it has been widely used in many fields such as social problems [50][51][52], artificial intelligence and control systems [53][54][55][56], image processing, medical diagnosis [57][58][59][60][61], and enterprise management [62][63][64][65]. Wang, Wei, and Wei [66] defined the 2TLNSs in which all decision values are expressed by 2TLs.…”

A novel approach toward skin cancer classification through fused deep features and neutrosophic environment

“…From images 3 and 4, we have more than one class code-word that is equivalent to one, so we apply the equation of crisp value to select the max crisp value in Step 3 in line. (20) In Algorithm 1, from images 5 and 6 in Table 4, we have all code-words equivalent to zero, so we selected the max indeterminacy value from Step 3 in. line (21) In Algorithm 1, from Table 4 for eight images, SVNSs predicted eight true classes and zero false classes, DarkNet predicted only three true classes and five false classes, and GoogleNet predicted six true classes and two false classes.…”

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

“…NS is an enhanced mathematical model that is applied to solve the problem of uncertain and ambiguous data. It includes a simple neutrosophic rule-based method in the form of; IF A AND B THEN Y to solve the problem rather than trying to design a system that is mathematically similar to the fuzzy classifier [48][49][50]. Thus, NT is similar to fuzzy technique.…”

“…Neutrosophic StageIn 1980, F. Smarandache introduced the neutrosophy theory, which is a branch of philosophy[48,49]. Neutrosophic technique (NT) uses neutrosophic sets and the principles of neutrosophic logic.…”