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.

中文翻译：

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心搏监护数据智能中智诊断系统


心搏监护数据的不确定性是生物医学领域分类的一项关键任务。通过机器学习算法构建良好且高效的分类器对于帮助医生诊断胎儿心率状态是必要的。所提出的中智诊断系统是基于反向传播算法的区间中智粗糙神经网络框架。它受益于中智集合论的优点，不仅提高了粗糙神经网络的性能，而且取得了比其他算法更好的性能。实验结果使用箱线图可视化数据，以便更好地理解属性分布。所提出框架的混淆矩阵的性能测量在准确率、精确率、召回率和F 1 分数方面分别为 95.1、94.95、95.2 和 95.1。 WEKA 应用程序用于分析不同算法（例如神经网络、决策表、最近邻和粗神经网络）的心胎监护数据性能测量。与其他算法的比较表明，所提出的框架是可行且高效的分类器。此外，接收者操作特征曲线分别以曲线下被认为高和可接受的 0.93、0.90 和 0.85 区域显示提出的病理、正常和可疑状态的框架分类。通过特征选择去除无效属性来改进所提出框架的性能测量将是未来适当的进步。此外，所提出的框架还可以用于各种现实问题，例如冠状病毒的分类、社交媒体和卫星图像。