article · Computational Intelligence and Neuroscience
Cardiotocography data presents significant uncertainty, making classification challenging in biomedical applications. To assist doctors in evaluating foetal heart rate states, an intelligent diagnostic framework was developed using an Interval Neutrosophic Rough Neural Network based on the backpropagation algorithm. This approach integrates neutrosophic set theory to enhance the performance of rough neural networks, outperforming comparative algorithms such as standard neural networks, decision tables, nearest neighbour methods, and standard rough neural networks tested in the WEKA platform. Experimental evaluations demonstrated an accuracy rate of 95.1 percent, precision of 94.95 percent, recall of 95.2 percent, and an F1-score of 95.1 percent. Receiver operating characteristic analyses confirmed strong performance across foetal states, recording areas under the curve of 0.93 for pathologic, 0.90 for normal, and 0.85 for suspicious classifications. Beyond obstetrics, the underlying framework could extend to classifying coronavirus, social media data, and satellite imagery.
Accurately assessing foetal heart rates during pregnancy helps medical staff detect complications early and respond appropriately. Cardiotocography data can often be ambiguous or inconsistent, complicating diagnoses. By applying advanced mathematical theories of uncertainty to machine learning, this diagnostic tool provides reliable classification of foetal health conditions, offering stronger technical support for clinical decision-making.
The framework could be integrated into clinical decision-support software to assist medical practitioners in interpreting cardiotocography records. Its algorithmic core could also target other sectors, including satellite imaging and social media analysis. Judged from the abstract, this represents early-stage algorithm development tested on analytical software, requiring further refinement such as feature selection and practical validation before direct commercial deployment.
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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 <i>F</i>1-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.
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DOI: 10.1155/2021/6656770
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