## 6 Conclusions and Future Work  
For the objective of early illness detection and treatment, a health service provider (HSP) system to collect patient data in real time has been created. Intelligent healthcare systems can move quickly to save many lives, especially when a patient is in a remote place without access to medical treatment. It is challenging to predict survival in patients with cardiac disease. Due to privacy and security concerns, it is hard to exchange user data when it comes to healthcare systems. In this paper, we proposed a hybrid federated learning framework for improved heart disease prediction and to address privacy issues in the healthcare system. In order to enhance heart disease prediction, the proposed framework combines MABC with RB-SVM feature optimization and classification techniques at the HSP's client node, while FedMA is used at the HSP global orchestrator to solve communication efficacy and privacy problems in the healthcare system. The main goal of this research is to shorten training time and improve communication efficiency while improving the prediction accuracy of heart disease. We evaluated and compared the performance in terms of several model prediction-based metrics and communication efficiency with the baseline FedMA, FedMA, and a developed upgraded version of FedMA using a GA-SVM optimizer and classifier algorithms in order to ensure the accuracy and validity of our proposed framework. Performance metrics including prediction accuracy, classification error, sensitivity, precision, and communication efficiency all showed a considerable improvement under the suggested paradigm. Our findings indicated that the suggested strategies produce outcomes with 1.5% greater accuracy, 1.6% lower classification error, 4.7% higher sensitivity, and 17.7% fewer rounds needed to reach the greatest degree of accuracy. In the future, we will focus on the rehabilitation and treatment of several additional serious illnesses including Parkinson's, diabetes, liver cancer, skin cancer, and breast cancer. Conceptualization, M.M.Y. and M.N. ; methodology, M.M.Y., M.A.K. and A.A.-R.; software, M.M.Y. and S.Q. ; validation, M.M.Y., M.N., A.-A.-R. and M.A.K. ; formal analysis, M.M.Y. and M.N. ; investigation, M.M.Y. and S.Q. ; resources, M.M.Y. and M.A.K. ; data curation, M.M.Y. and M.N. ; writing--original draft preparation, M.M.Y., M.N. and M.A.K. ; writing--review and editing, M.M.Y., M.N., S.Q. and M.A.K. ; visualization, M.M.Y. and M.A.K. ; supervision, M.N. ; project administration, M.N., S.Q., A.A.-R. and M.A.K. ; funding acquisition, A.A.-R. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **Accuracy** & **F-Measure** & **Precision** & **Classification Error** & **Sensitivity** & **Specificity** \\ \hline FedAvg-SVM & 91.3 & 87.3 & 92.3 & 20.4 & 85.3 & 59.5 \\ \hline FedMA-SVM & 91.7 & 88.4 & 90.1 & 18.6 & 89.5 & 72.5 \\ \hline FedMA with GA-SVM & 92.3 & 89.6 & 93.7 & 13.3 & 91.9 & 78.8 \\ \hline FedMA with MABC-RB-SVM (Proposed) & **93.8** & **90.1** & **94.2** & **11.9** & **96.6** & **81.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance on features of the dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Max. Accuracy Achieved** & **\# Of Rounds to Reach 91\%** & **Difference** \\ \hline FedAvg-SVM & 91.3 & 3810 & – \\ \hline FedMA-SVM & 91.7 & 3425 & 10.1\% \\ \hline FedMA with GA-SVM & 92.3 & 3046 & 20.1\% \\ \hline FedMA with MABC-RB-SVM (Proposed) & **93.8** & **2408** & **37.8\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Consumed algorithm time for the highest accuracy of the model. **Funding:** Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R235), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.