## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Results and Comparison
First, there is an interaction mechanism from medical services to HISs, facilitating the seamless flow of data and information from the healthcare service delivery to the intelligent information-processing systems. Contrastingly, a service model strategy performs from IISs to medical services, guaranteeing that the technological abilities and perspectives provided by these frameworks are efficiently incorporated into the generation of medical services. In addition, there presents a pivotal technical support link from IISs to health management. This element guarantees that the progress and technical capacities extracted from IISs contribute to the strategic management of health services. Simultaneously, an interface design technique performs from health management to IISs, enabling the perspectives and demands of health management to form the model and performances of the IISs. The model is thereby enriched by a relation method from medical services to health management, simplifying the direct flow of information from the delivery of medical services to the wider range of health management. This guarantees that real-time data and visions from patient care directly inform strategic health management decisions. Furthermore, a design implication strategy performs from health management to medical services, integrating strategic perspectives and planning from health management into the design and delivery of medical services. So, these dynamic relations and interfaces establish a holistic and incorporated system that uses IISs to increase the efficiency and  
Figure 9: Main categories of healthcare systems based on their function. productivity of multi-objective healthcare management. Figure 10 represents the dynamic system of intelligent healthcare management. As indicated in Section 4, ISs for multi-objective healthcare management can be categorized into six distinct groups, each serving a crucial role in the enhancement of healthcare processes. Diagnostic systems play a pivotal role in accurate and timely disease identification, aiding healthcare professionals in early intervention. Treatment-planning systems contribute by offering personalized and efficient treatment strategies and optimizing patient outcomes. Patient monitoring systems guarantee frequent monitoring of health parameters, enabling the real-time arrangement of treatment plans. Resource allocation systems streamline the distribution of medical resources, optimizing functional effectiveness. Moreover, preventive healthcare systems concentrate on proactive measures to reduce health risks. Whereas these IISs bring important advantages, like developed detection and individualized care, they come with downsides and restrictions. Issues might contain data privacy concerns, algorithm biases, and the demand for robust validation procedures. Dwelling deeper into these facets is important for a thorough comprehension of the implications of ML in multi-objective healthcare management. Figure 11 demonstrates a variety of ISs for multi-objective healthcare management. As indicated in Figure 12, according to our evaluation, treatment-planning systems have the highest accuracy among all other information systems, with an outstanding rate of 93.12%. This superior accuracy is initially owing to their capability to incorporate and evaluate an extensive array of patient-specific data, containing diagnostic data, medical guidelines, and possible treatment results. These models apply developed algorithms and DL methods to assess various purposes simultaneously, ensuring that the selected treatment plan is optimized for the patient's unique demands. Their efficiency in multi-objective healthcare management lies behind their capacity to balance diverse metrics like treatment effectiveness, potential side influences, and patient preferences, leading to highly individualized and accurate treatment recommendations. Moreover, based on Figure 13, treatment-planning systems indicate the highest precision among all ML-powered information systems, with a notable precision rate of 92.5%.