Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Initially, the privacy of the medical data is ensured by using the key matrix developed based on the privacy utility coefficient matrix using the chronological-Whale optimization algorithm. The privacy protected data are subjected to classification by using ACNN that performs the optimal classification using the proposed TGD algorithm. The proposed TGD algorithm is the integration of Taylor series in the gradient descent algorithm that updates the optimal weight of ACNN based on the weights in the previous iterations. The analysis using the Cleveland, Switzerland, and Hungarian dataset proves that the proposed classification strategy obtains an accuracy of 0.9252, a sensitivity of 0.8419, and a specificity of 0.8387, respectively.

中文翻译：

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基于泰勒和梯度下降的演员评论神经网络，用于对隐私保留的医学数据进行分类。

隐私保护的医学数据的分类是研究人员的工作，因为它激起了向第三方身份验证器隐藏敏感数据的重要性。确保病历的私密性和使用疾病预测机制在人们的生活中起着举足轻重的作用，因此需要早期发现疾病以进行早期诊断。因此，本文提出了一种基于泰勒梯度下降（TGD）的演员批评者神经网络（ACNN）的方法，该方法集中于执行医学数据分类。最初，通过使用基于时间-鲸鱼优化算法的，基于隐私效用系数矩阵开发的密钥矩阵来确保医疗数据的隐私。通过使用建议的TGD算法执行最佳分类的ACNN，对受隐私保护的数据进行分类。提出的TGD算法是泰勒级数在梯度下降算法中的集成，该算法基于先前迭代中的权重更新ACNN的最佳权重。使用克利夫兰，瑞士和匈牙利的数据集进行的分析证明，所提出的分类策略分别获得0.9252的准确度，0.8419的敏感性和0.8387的特异性。