## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction  
A growing body of research is addressing the application of federated learning in the area of disease prediction and mortality forecasting. The results of these research efforts are clearly summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Application** & **Methods** & **Results** & **Limitations** \\ \hline
[85] & 2022 & Prediction of ICU mortality risk & Deep federated learning, centralized machine learning, and local machine learning. & Federated learning performs equally well as the centralized approach. Federated learning is substantially better than the local approach. & - The generalizability of the approach beyond the MIMIC-III dataset needs to be tested. - Does not address the performance of the FL workflow in predicting ICU mortality at an early stage using other datasets. - Does not discuss potential challenges or limitations of implementing FL in a healthcare setting. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Federated learning for early detection of the onset of a disease and mortality prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Year** & **Application** & **Methods** & **Results** & **Limitations** \\ \hline
[20] & 2023 & Review of federated learning being used for early health prediction in diseases such as cardiovascular disease, diabetes, and cancer. & Federated learning and FL aggregation algorithms. & Federated learning helps solve privacy concerns in machine learning; 32\% of companies plan to implement federated learning. & - FL in disease prediction is still in the early stages. \\ \hline
[86] & 2023 & Early prediction of cardiovascular disease. & Modified artificial bee colony optimization with Support Vector improves the prediction accuracy (MABC-SVM). & The proposed hybrid technique for federated learning improves the prediction accuracy & - The paper does not mention any limitations of the proposed hybrid classifier-based FL framework. \\ \hline
[87] & 2022 & Federated learning to predict heart disease by training a shared model while keeping patient data distributed across multiple locations. & Logistic regression and Support Vector benchmark dataset. & Accuracy of around 89\% on the UCI benchmark dataset. & - The paper does not provide details on the size or diversity of the dataset used for training the shared model. & - The paper does not provide a comparison of the FL approach with other existing machine learning methods for heart disease prediction. \\ \hline
[88] & 2022 & Federated learning to predict chronic kidney diseases & Federated learning and image processing technique to identify the affected area. & Higher accuracy, efficiency, specificity, and sensitivity. Increased accuracy through training with a single image. & - No mention of the size or diversity of the decentralized data used for training the algorithm. \\ \hline
[88] & 2022 & Combining the IoT and blockchain in healthcare. Focus on individualized health monitoring and early disease detection using wearable gadgets. & Combining the IoT and blockchain technologies and applying blockchain against smarter simulations, lower latency, lower power consumption, and privacy. & - Limited battery life of IoT devices is a major obstacle to integrating blockchain with the IoT. \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._