## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Results and Comparison
This high level of precision is associated with their complex algorithms that thoroughly evaluate patient-specific data, medical guidelines, and potential treatment results. In multi-objective healthcare management using ML-powered information systems, these models excel by meticulously weighing and balancing various metrics, like treatment efficiency, the risk of side effects, and personal patient preferences. The outcome is a finely tuned treatment plan that is not only personalized but also optimized for the best potential result,  
Figure 10: Dynamic system of intelligent healthcare management. On the other hand, as demonstrated in Figure 14, treatment-planning systems gain the highest F1 score among all ML-based information systems, with a remarkable score of 92.66%. The F1 score, which balances precision and recall, highlights the model's capacity to make accurate and dependable decisions while minimizing both false positives and false negatives. In multi-objective healthcare management using ML-based information systems, treatment-planning systems are distinguished by incorporating complicated patient information, medical protocols, and predictive methods. This incorporation enables the system to efficiently map trade-offs among competing purposes, like maximizing treatment effectiveness and minimizing side effects. The outcome is a well-balanced method that ensures both high precision and high recall, enabling the highest F1 score in this significant medical application. Figure 14: The average F1 score of the studied paper. Figure 13: The average precision of the studied paper. Nevertheless, as represented in Figure 15, treatment-planning systems have the highest sensitivity among all information systems, with a notable rate of 93.06%. Sensitivity, which measures the model's capacity to correctly detect positive cases, is pivotal in ensuring that no potential treatment option is overlooked. The high sensitivity in these models lies behind their capability to extensively evaluate patient information, medical histories, and diverse treatment potentials. In multi-objective healthcare management using ML-powered information systems, this sensitivity enables treatment-planning systems to recognize and consider even the most subtle metrics that can affect patient results, ensuring that the most efficient treatment options are detected and prioritized. This comprehensive method aids in minimizing the risk of under-treatment and ensures that patient care is both proactive and precise. On the other side, according to Figure 16, patient monitoring systems show the highest specificity through all of the information systems applied for healthcare management, with a specificity rate of 92.78%. Specificity measures the method's capacity to truly recognize negative cases, ensuring that unnecessary interventions are minimized. This high specificity is important for preventing false positives, which could cause overtreatment or unnecessary alarms. In ML-powered information systems across multi-objective healthcare management, patient monitoring systems attain this high specificity by ongoing and accurately monitoring a broad domain of crucial patient health demonstrators, distinguishing between regular fluctuations and the true symptoms of deterioration. By emphasizing real-time data and using strict thresholds for alerts, these models ensure that only the most related and significant variations in a patient's situations trigger a response, promoting highly accurate decision-making and decreasing the simila  
Figure 15: The average sensitivity of the studied paper. Moreover, according to Figure 17, diagnostic systems demonstrate the highest computational efficiency among all smart information systems, with a notable effectiveness rate of 93.83%. This high computational efficiency allows these models to process huge amounts of data rapidly and meticulously, promoting fast diagnoses and decision-making.