## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Results and Comparison
As a result, the comparative evaluation shows that, when most investigations show high effectiveness and productivity, those leveraging professional ML methods, such as deep reinforcement learning and evolutionary algorithms, intend to operate better in complicated and dynamic scenarios. The effectiveness percentages range from 79% to 93%, and the efficiency percentages range from 82% to 93%, emphasizing the efficiency of these methods within various main factors and application areas. Python stands out as the most well-known programming language in this field. Its familiarity is owing to its versatility, rich libraries, and user-friendly syntax, which make it a great option for creating simulation systems in healthcare administration. Python's wide environment, including libraries like TensorFlow, PyTorch, and sci-kit-learn, enables the implementation of various ML algorithms that are fundamental for healthcare simulations. The language's familiarity lies in its adaptability to a diverse application, simplicity of incorporation with data-processing devices, and prevalent adoption in the ML community. In the particular area of healthcare management, Python's dominance is highlighted by its capacity to generate a seamless and effective platform for improving complex ML systems, thereby increasing the simulation abilities that are fundamental for multi-objective healthcare management studies. Figure 8 figures out the frequencies of the programming languages used for simulating the proposed methods of the investigated papers. Hybrid models serve an evolutionary role in healthcare by incorporating diagnostics, monitoring, and care performances through a unified model, allowing a seamless and comprehensive model of patient treatment. These models apply ML to simultaneously evaluate diagnostic data, monitor patient conditions in real-time, and propose tailored care recommendations, addressing several healthcare goals effectively. For example, hybrid systems can identify illnesses early across diagnostic abilities, monitor patient improvement by leveraging continuous tracking, and adapt care protocols dynamically according to real-time data. This incorporation not only improves the precision and individualization of treatment but also optimizes resource allocation and mitigates the time needed for healthcare decision-making. By bridging traditionally separate areas, hybrid models exemplify the future of multi-objective healthcare management, proposing innovative solutions to complicated healthcare issues. However, healthcare systems consist of two primary categories, classified based on their functionalities, namely IS for patient care and managerial IS. IS for patient care is subdivided into three essential sections, namely PMS, clinical information systems (CIS), and systems dedicated to clinical support services. PMS comprises several components,  
Figure 8: Frequency of programming languages used for simulating the proposed methods of the investigated papers. such as patient registration applications, client registration, visit registration, and client-resource management applications encompassing appointments, scheduling, resource allocation, and patient tracking, as well as charging, billing, and payment applications. On the other hand, clinical support services systems focus on maintaining a patient information database, prominently featuring electronic medical records (EMR). These EMRs serve as comprehensive repositories for patient health data, facilitating seamless information access and exchange among healthcare professionals to enhance the overall quality and coordination of patient care. Figure 9 shows the main categories of healthcare management using ML-based ISs based on their function. Notwithstanding this, the dynamic system of intelligent health encompasses three interrelated components, namely medical services, IISs, and health management. The system operates through a set of well-defined interactions and interfaces aimed at optimizing healthcare delivery.