## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Hybrid Systems  
Hybrid systems are developed models that incorporate several ML systems, methodologies, and data sources to address the complicated and multi-objective issues of healthcare management. By integrating various techniques, like DL for diagnostics, optimization methods for decision support systems, and resource allocation for treatment planning, these models improve effectiveness, adaptability, and individualized treatment. They allow for a comprehensive method of healthcare by adjusting competing objectives, such as real-time patient care optimization, diagnostic accuracy, and cost-effectiveness. For example, hybrid models \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**FactorsAuthors**} & \multirow{2}{*}{**[PERSON], [PERSON] [56]**} & \multicolumn{2}{c}{**[PERSON] and**} & \multicolumn{2}{c}{**[PERSON],**} & \multicolumn{2}{c}{**[PERSON],**} & \multicolumn{2}{c}{**[PERSON] and**} \\  & & & **[PERSON] [57]** & **[PERSON] [58]** & **[PERSON] [59]** & **[PERSON] [59]** & **[PERSON] [60]** \\ \hline \multirow{5}{*}{**Factors**} & \multirow{2}{*}{**Accuracy**} & 90.8\% & 92.3\% & 94.7\% & 92.3\% & 89.5\% \\ \cline{2-6}  & & **Precision** & 91.2\% & 91.8\% & 93.5\% & 91.7\% & 87.8\% \\ \cline{2-6}  & & **F1 Score** & 90.9\% & 92.0\% & 94.1\% & 92.0\% & 88.6\% \\ \cline{2-6}  & & **Sensitivity** & 90.7\% & 92.1\% & 94.6\% & 92.5\% & 90.2\% \\ \cline{2-6}  & & **Specificity** & 91.0\% & 92.2\% & 94.8\% & 92.1\% & 88.4\% \\ \cline{2-6}  & & **Computational** & 87.5\% & 89.6\% & 90.4\% & 88.9\% & 84.7\% \\ \cline{2-6}  & & **AUC-ROC** & 91.3\% & 92.5\% & 95.2\% & 93.2\% & 91.3\% \\ \hline \multirow{5}{*}{**Dataset**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**PM2.5 Air Quality**} & \multirow{2}{*}{Health Risk Analysis**} & \multirow{2}{*}{IoMT Patient Monitoring} & \multirow{2}{*}{Heart Disease Dataset} & Influenza \\  & & & & & & & Vaccination \\ \cline{1-1} \cline{2-6}  & & **Size of Dataset** & 400 samples & 300 samples & 50 samples & 303 samples & 100 samples \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Specifications of papers studied preventive healthcare systems using AI-driven multi-objective decision-making. can incorporate image-based diagnostics with RL for treatment recommendations, ensuring accuracy and functional effectiveness. Their capability to harmonize treatment planning, diagnostics, and monitoring across a unified model makes them indispensable for dynamic healthcare environments, and proposing actionable and scalable solutions that can be adopted for transforming patient demands, as they are data-rich. By the same token, [PERSON] and [PERSON] [22] proposed a technique that is defined as a hybrid technique incorporating decision theory and ML. Their application area for this strategy is customs fraud control in Spain. Their strategy was modeled to solve several goals that are relevant to fraud detection and control throughout the customs field. The authors argued the fundamentals of the MOBADO (a multi-objective Bayesian approach with dynamic optimization (MOBADO), which is a hybrid of decision theory and ML used for customs fraud control in Spain) method, its use in customs fraud control, and how the incorporation of decision theory and ML contributes to more-efficient and dynamic optimization for solving the issues in fraud control procedures. On the other hand, [PERSON] and [PERSON] [61] presented a hybrid method for dermatologic illness detection. The presented strategy integrated PSO with multi-objective clustering strategies to increase the diagnostic procedure for dermatologic diseases. PSO is used for optimization, emphasizing feature selection or system factors, while multi-objective clustering is applied to improve the grouping of dermatologic data. This hybridization aimed to achieve more accurate and comprehensive diagnoses by optimizing the selection of relevant features and enhancing the clustering of dermatologic data, contributing to the overall effectiveness of the diagnostic system.