## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.5 Evaluation Metrics of Edge AI Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Application** & **Methods** & **Results** \\ \hline
[45] & 2022 & Identification and prediction of chronic diseases using machine learning. & Data collection from various sources. Training with CNN and KNN models outperformed Naive Bayes, decision tree, and logistic regression algorithms. CNN and KNN achieved a higher precision, recall, and F1-score. \\ \hline
[75] & 2022 & A survey on Federated Learning for Privacy & FL for privacy preservation in IoMT. Advanced FL architectures incorporating DRL, digital twins, and GANs & Description of some advanced FL architectures incorporating DRL, digital twins, and generative adversarial networks (GANs) for detecting privacy threats. \\ \hline
[72] & 2022 & Review of Federated Learning for Healthcare & Systematic literature review on FL methodology. Definition of research questions & Systematic literature review on FL in healthcare. Proposed architecture for FL applied to healthcare data \\ \hline
[63] & 2022 & Machine learning models used for early diabetes prediction. & SVM-ANN ensemble. SVM, KNN, NB, C4.5 DT, Adaboost DT with Bagging, Bagged DT, K-means & Random forest (RF) model achieved the highest accuracy of 82.26% in predicting diabetes. The Naive Bayes (NB) model performed the worst, with an accuracy rate of 70.56\%. \\ \hline
[59] & 2019 & Health risk prediction models incorporating personality data. & informal rule for cross-validation error. Anti-conservative approach to protect against overfitting & Four-year incidence rate of possible MCI is roughly 20\%. Model C with personality data shows a significant improvement in overall performance. \\ \hline
[66] & 2015 & Comparison of predictive models for early hospital readmissions. & Logistic regression with maximum likelihood estimator. Logistic regression with multi-step heuristic approach & Random forest and penalized logistic regressions are the best methods for predicting early readmissions. Deep learning methods outperform regression methods in the healthcare literature. \\ \hline
[67] & 2021 & Predicting mortality among patients with liver cirrhosis. & Deep neural network (DNN), random forest (RF), and logistic regression (LR) algorithms. Multiple imputation for missing values in variables. & Models with all variables outperformed those with four MELD-NA variables. The DNN model achieved a higher AUC than the LR and RF models. \\ \hline
[76] & 2022 & Clinical prediction models to estimate disease probability and health outcomes. & Split-sample method for model development and internal validation. Resampling methods, especially the bootstrap method, for stable estimates \\ \hline
[49] & 2023 & AI models for predicting and early diagnosis of pancreatic cancer. & Scoping review conducted following PRISMA-ScR guidelines. Two reviewers independently performed study selection and data extraction \\ \hline
[64] & 2022 & Edge computing-based heart disease prediction. & Decision-tree-based classifier for analyzing health data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Edge AI for early detection of the onset of a disease and mortality prediction.