## 7 Conclusion and Future Scope  
IoT consists of various networking things, such as intelligent and smart devices that are interconnected to generate meaningful information in the network without the need for human interaction. Recently, the healthcare system has been connected over the IoT paradigm for facilitating better possibilities for monitoring patients, effective diagnosis and the proper treatment of existing diseases. This research is focused on the classification system in the healthcare system by enabling IoT to collect patient data and classify cancer diseases using those data. A hybrid optimization algorithm was developed using SPWO for classifying cancer disease such that the proposed model was devised using SPBO and WOA. The proposed method optimally generates better classification results by extracting a unique set of features automatically using the Fisher score model. However, the selection of optimal features shows an increase in performance, as the best or optimal features help the classification strategy to be more robust and unique rather than considering the entire set of features. The developed scheme eliminates local optimal solutions and moves toward a global solution that in turn increases convergence rate and improves testing accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Nodes** & **Metrics** & **Deep Learning** & **CNN** & **Hybrid DNN** &
\begin{tabular}{c} **Proposed SPWO** \\ **Deep Maxout Network** \\ \end{tabular} \\ \hline \multirow{3}{*}{100 Nodes} & Testing accuracy & 0.799 & 0.816 & 0.890 & 0.931 \\ \cline{2-5}  & Sensitivity & 0.810 & 0.833 & 0.911 & 0.953 \\ \cline{2-5}  & Specificity & 0.770 & 0.796 & 0.883 & 0.915 \\ \hline \multirow{3}{*}{150 Nodes} & Testing accuracy & 0.791 & 0.809 & 0.890 & 0.928 \\ \cline{2-5}  & Sensitivity & 0.808 & 0.828 & 0.910 & 0.949 \\ \cline{1-1} \cline{2-5}  & Specificity & 0.768 & 0.791 & 0.878 & 0.910 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of proposed model with existing models.  
Figure 6: Analysis using 150 nodes.  
The results of the methodology are analyzed by varying the training and testing percentage and the results are evaluated by performing a comparative analysis and a performance analysis for different numbers of nodes. However, the proposed strategy acquires better execution in terms of measures, such as testing exactness, responsiveness, and explicitness with the result values of 0.931, 0.953, and 0.915 by 100 nodes.  
These results are better than those of other existing methods. In the proposed method, only one feature selection method is introduced whereas in the future, the better performance can be shown by designing an optimal feature selection mechanism using hybrid distance measures to further improve the performance. Further, the proposed model could be improvised to increase the accuracy, sensitivity, and specificity of the system by introducing a new optimization technique-based classification. Moreover, other fatal diseases can be diagnosed using the methodology. In the future, research can contribute to designing optimal feature selection mechanisms using hybrid distance measures to further improve the performance.  
Conceptualization, J.P.; Methodology, J.P. and S.K.; Software, J.P. and S.K.; Validation, J.P. and S.K.; Formal analysis, J.P.; Investigation, S.K.; Resources, S.K.; Data curation, J.P.; Writing--original draft, J.P.; Writing--review & editing, J.P.; Supervision, S.K.; Project administration, S.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
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The authors declare no conflict of interest.