## 5 Experimental Evaluation and Validation
### Results and Discussion  
In the FL-based HSP environment, the communication rounds refer to the number of times the HSP client model parameters are exchanged during the training process. If the large number of rounds are consumed by an FL model, then it will also increase the communication overhead and computational cost. Therefore, the number of communication rounds is a key aspect in the FL system's overall performance and efficiency. The impact of communication rounds on the algorithm's accuracy in making predictions on the combined \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S\#** & **Feature** & **Explanation** & **Unit** & **Coded Values** \\ \hline \multirow{3}{*}{1} & Resting blood & & & Low Level = Below 120 = \(-\)1 \\  & pressure (Rt\_Bp) & & & & Normal Level = 120\(-\)139 = 0 \\  & & & & High Level = Above 139 = 1 \\ \hline \multirow{3}{*}{2} & Cholesterol & & & & \textless{}200 mg/dL = Low = \(-\)1 \\  & serum (CL\_S) & & & 200–239 mg/dL = Normal = 0 \\  & serum (CL\_P) & & & \textgreater{}240 mg/dL = High = 1 \\ \hline \multirow{3}{*}{3} & Chest pain (C\_P) & Type of chest & & & Angina Typical (AT) = 2 \\  & & pain & & & Asymptomatic (AS) = 1 \\  & & pain & & Angina Atypical (ATA) = 0 \\  & & & & Non-Angina (NA) = \(-\)1 \\ \hline \multirow{3}{*}{4} & Max heart rate & Maximum & & \textless{}69 bpm = Low = \(-\)1 \\  & (MHR) & achieved heart & Integer & 70–90 bpm = Normal = 0 \\  & rate in bpm & & \textgreater{}91 bpm = High = 1 \\ \hline \multirow{3}{*}{5} & Depression level & Old peak in ST & & \\  & (Dp\_L) & (numeric value & & \\ \cline{1-1}  & (Dp\_L) & measured for & & \textgreater{}0.5 mm = High = 1 \\  & & depression level) & & \\ \hline \multirow{3}{*}{6} & Resting electrocardiogram & Normal, ST T, or & & LVH = 2 \\  & cardiogram (Rt\_ECG) & & LVH & & ST = 1 \\ \hline \multirow{3}{*}{7} & Angina induced & & & & Normal = 0 \\  & by exercise (AI\_bE) & Yes or No & String & & Yes = 1 \\  & & & & No = 0 \\ \hline \multirow{3}{*}{8} & Fasting blood & & & & True = 1 \\  & sugar (F\_BS) & & & False = 0 \\ \hline \multirow{3}{*}{9} & ST slope (ST\_S) & Peak exercise & & & Up = 2 \\  & & slope & & Flat = 1 \\  & & slope & & Down = 0 \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{Age (A)} & Age in years & Integer & \textgreater{}77 = 2, 64–77 = 1, 47–63 = 0, & \\  & & & & 35–46 = \(-\)1, \(\textless{}35\) = \(-\)2 \\ \hline \multirow{3}{*}{11} & Sex (S) & Female and Male & String & Female = 0, Male = 1 \\ \hline \multirow{3}{*}{12} & Target (heart & & & Yes = 1, \\  & disease) & Yes or No & Integer & No = 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Thorough description of the combined dataset. dataset is seen in Figure 3. Our proposed framework reaches 93.8% accuracy within the 4500 rounds of communication, which is better than the existing FedAvg-SVM, FedMA-SVM, and FedMA with GA-SVM algorithms. Since the proposed MABC-RB-SVM method employs the hybrid of the MABC optimizer and RB-SVM classifier for optimal feature selection and classification at HSP clients, and for the HSP global orchestrator, we deploy the FedMA which permits our overall model to accomplish better accuracy in a lesser number of communication rounds than the existing FL algorithms. In the FedAvg-SVM and FedMA-SVM, the HSP overall model performs the simple averaging and matched averaging, respectively, on the simple SVM kernel at their client model algorithm which results in consuming higher communication rounds. The learning rate is increased in the GA-SVM with FedMA, but the convergence is consumed more by the classification and feature selection. Consequently, the proposed hybrid framework accomplishes better accuracy and reduces the amount of communication rounds used. We evaluate and vary the local epochs E from 10 to 160, to examine the impact of local epochs on the prediction accuracy of the proposed hybrid technique and existing FL algorithms. The accuracy test on each E of the proposed hybrid framework, FedAvg-SVM, FedMA-SVM, and hybrid of GA-SVM with FedMA, is reviewed and compared.