Rheumatic Fever or Acute Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD) are commonly suffered by people of all ages around the world. RF usually begins with strep throat infections, skin problems, joint pain etc. Clinical tests and medical informatics are useful to save patients from Streptococcus Group-A bacteria using medical treatments and detection mechanism. Clinical data based statistical analysis approaches assist medical diagnosis model in detecting diseases based on standard decision. Standard decision making frameworks are effective when using these approaches. Nevertheless, existing mechanisms cannot detect or predict the incidence of strep throat infection at an earlier stage based on unclear datasets or complex datasets. The recent computational healthcare informatics systems use Machine Learning (ML) and Deep Learning (DL) techniques, but they are not sufficient in terms of their field integration. The proposed work implements a nonlinear recurrent auto encoder using complex data evaluation procedures to accurately detect and predict the strep throat infections accurately. In this regard, the proposed model builds on the technical features of Denoised Variational Stacked Auto Encoders (DVSE), nonlinear regression computations and Long Shot Term Memory (LSTM) to perform recurrent data analysis practices. Compared with the existing techniques such as Blood Serum Content Analysis model (BSCA), Smartphone based Strep Throat Detection (SSTD) approach and Logistic Regression Based RF Detection approach (LRRF), the multi-level medical data evaluation (throat images and blood samples) provides 10–15% more accuracy for strep throat detection. As shown in the implementation section, the proposed nonlinear recurrent system has the potential to detect strep throat infections early.

风湿热或急性风湿热（RF）和风湿性心脏病（RHD）是世界各地各个年龄段人群的常见病。 RF 通常始于链球菌性咽喉炎感染、皮肤问题、关节疼痛等。临床测试和医学信息学有助于通过医疗治疗和检测机制来拯救患者免受 A 族链球菌细菌的侵害。基于临床数据的统计分析方法协助医学诊断模型根据标准决策检测疾病。使用这些方法时，标准决策框架是有效的。然而，现有机制无法根据不清楚的数据集或复杂的数据集在早期阶段检测或预测链球菌性咽喉炎感染的发生率。最近的计算医疗信息学系统使用机器学习（ML）和深度学习（DL）技术，但它们在现场集成方面还不够。所提出的工作使用复杂的数据评估程序实现了非线性循环自动编码器，以准确地检测和预测链球菌性咽喉炎感染。在这方面，所提出的模型基于去噪变分堆叠自动编码器（DVSE）、非线性回归计算和长短期记忆（LSTM）的技术特征来执行循环数据分析实践。与血清含量分析模型（BSCA）、基于智能手机的链球菌咽喉检测（SSTD）方法和基于逻辑回归的射频检测方法（LRRF）等现有技术相比，多级医疗数据评估（咽喉图像和血液样本）链球菌性咽喉炎检测的准确度提高 10-15%。如实施部分所示，所提出的非线性循环系统具有早期检测链球菌性咽喉炎感染的潜力。