## 2 Related Works
The accuracy of this method in experiments reached 99.67%. To protect the data privacy of healthcare applications that rely on the Internet of Things [19], [PERSON] et al. proposed a deep federated learning framework for healthcare data monitoring and analysis using IoT devices. This framework achieved an accuracy of 84.8% and an AUC (Area Under the Curve) of 97% in experiments on detecting skin diseases. To solve the privacy and security issues during interactions caused by potential patient data leakage in the healthcare Internet of Things [20], [PERSON] et al. proposed a decentralized, efficient, and privacy-enhanced federated edge learning system based on convolutional neural networks. This system achieved an accuracy of 87% in experiments on the skin cancer data set. [PERSON] et al. proposed a collaboration framework [21]. This framework is used to train convolutional neural network-based heart disease prediction models. This framework uses federated learning to implement model training using distributed data stored individually on multiple machines. This framework uses the MIT-BIH arrhythmia data set to train the model. In experiments, this framework achieved an accuracy of 98.92%, a recall of 97.41%, a precision of 99.23%, and an F1 score of 98.02%. To solve the security and privacy issues of the medical Internet of Things under edge intelligent computing [22], [PERSON] et al. proposed a privacy protection scheme for federated learning under edge computing. [PERSON] et al. proposed an end-to-end system integrating multimodal sensors and federated learning algorithms to achieve digital biomarker detection of multidimensional [PERSON]'s disease in natural living environments [23]. The system achieved an accuracy of 95% in experiments detecting digital biomarkers and an average accuracy of 87.5% in experiments identifying [PERSON]'s disease. [PERSON] et al. proposed a privacy-preserving federated learning method based on homomorphic encryption and deep neural network (DNN) for the Internet of Things healthcare system [24]. This method achieved an accuracy of 76.9% in experiments using the HAM10000 data set. As can be seen from the above description and Table 1, current research based on machine learning, federated learning, and edge computing has achieved good results. For example, the accuracy of predicting the risk of cardiovascular disease reaches 99.67%, the accuracy of predicting the risk of skin cancer reaches 87%, and the accuracy of predicting the risk of heart disease reaches 98.92%. However, in the above studies, the neural network model parameters used are too large when predicting disease risk. In the application scenarios of federated learning and edge computing, they are limited by hardware resource requirements and are challenging to promote widely. The lightweight neural network model designed in this article has only 7.63 MB of parameters and only occupies 155.28 MB of memory. In the experiment, the accuracy of stroke risk prediction was 96%, the precision rate was 95%, the recall rate was 93%, the specificity was 95%, the F1 score was 98%, and the AUC was 97%. This experimental result shows that the distributed intelligent edge computing technology proposed in this article for disease risk prediction can provide practical support for disease risk prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Researcher** & **Research Objectives** & **Basic Algorithm** & **Accuracy** \\ \hline Q. [PERSON] et al. [14] & Health Monitoring & CNN & 95.87\% \\ [PERSON] et al. [15] & COVID-19 & GAN & 97.5\% \\ [PERSON] et al. [17] & Cardiovascular Diseases & BiLSTM & 99.67\% \\ [PERSON] et al. [20] & Skin Cancer & CNN & 87\% \\ [PERSON]. [PERSON] et al. [21] & Heart Disease & CNN & 98.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related Research Statistics.