Intelligent Neutrosophic Diagnostic System for Cardiotocography Data

Autor: Khaled Mahfouz, Ibrahim El-Henawy, A. A. Salama, Belal Amin, Mona G. Gafar
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Support Vector Machine
Cardiotocography
Article Subject
General Computer Science
Computer science
General Mathematics
0206 medical engineering
Computer applications to medicine. Medical informatics
R858-859.7
Feature selection
Neurosciences. Biological psychiatry. Neuropsychiatry
02 engineering and technology
computer.software_genre
Field (computer science)
k-nearest neighbors algorithm
Machine Learning
Artificial Intelligence
Predictive Value of Tests
Classifier (linguistics)
0202 electrical engineering
electronic engineering
information engineering
Humans
Artificial neural network
General Neuroscience
Decision Trees
COVID-19
Reproducibility of Results
Confusion matrix
General Medicine
020601 biomedical engineering
Backpropagation
020201 artificial intelligence & image processing
Neural Networks
Computer
Data mining
Decision table
computer
Algorithms
Research Article
RC321-571
Zdroj: Computational Intelligence and Neuroscience, Vol 2021 (2021)
Computational Intelligence and Neuroscience
ISSN: 1687-5273
1687-5265
Popis: 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.
Databáze: OpenAIRE
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