learning algorithms. In this paper, we review the classification algorithms used in the health care system (chronic diseases) and present the neural network-based Ensemble learning method. We briefly describe the commonly used algorithms and describe their critical properties. Materials and Methods: In this study, modern classification algorithms used in healthcare, examine the principles of these methods and guidelines, and to accurately diagnose and predict chronic diseases, superior machine learning algorithms with the neural network-based ensemble learning Is used. To do this, we use experimental data, real data on chronic patients (diabetes, heart, cancer) available on the UCI site. Results: We found that group algorithms designed to diagnose chronic diseases can be more effective than baseline algorithms. It also identifies several challenges to further advancing the classification of machine learning in the diagnosis of chronic diseases. Conclusion: The results show the high performance of the neural network-based Ensemble learning approach for the diagnosis and prediction of chronic diseases, which in this study reached 98.5, 99, and 100% accuracy, respectively.

翻译：学习算法。 在本文中, 我们审查医疗体系( 慢性疾病) 使用的分类算法, 并展示基于神经网络的学习方法。 我们简单描述常用算法, 并描述其关键特性。 材料和方法 : 在这项研究中, 医疗体系中使用的现代分类算法, 检查这些方法和指南的原则, 准确诊断和预测慢性病, 使用神经网络共性学习的高级机器学习算法。 为此, 我们使用实验数据, 以及UCI 网站上关于慢性病人( 糖尿病、 心脏、 癌症) 的真实数据。 结果: 我们发现, 用于诊断慢性病的群算法比基线算法更有效。 它还确定了在进一步推进慢性疾病诊断中的机器学习分类方面存在的几项挑战。 结论: 研究结果显示,基于神经网络的用于诊断和预测慢性病的强化学习方法表现良好, 这项研究的准确率分别达到98.5、 99 和 100% 。