## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Preventive Healthcare Systems  
In preventive healthcare systems, AI particularly increases multi-objective healthcare management by allowing the early detection and avoidance of diseases, therefore developing population health results. AI algorithms evaluate large-scale health data, containing genetic data, lifestyle metrics, and ecological impacts, to detect persons at risk of improving chronic situations [87]. By processing these various data, AI-driven systems may forecast disease outbreaks, recommend individualized preventive measures, and optimize vaccination schedules. These models aid healthcare practitioners in making data-driven decisions that balance several goals, like decreasing healthcare costs, developing patient health results, and minimizing the obstacle of disease in the healthcare system. For example, AI may detect high-risk populations and dedicate resources for objected interventions, avoiding diseases before they progress into worse situations [88]. Additionally, AI in preventive healthcare aids in outlining public health techniques that are tailored to the demands of special communities, ensuring that resources are allocated effectively and equitably. However, the efficiency of AI-driven preventive healthcare systems is contingent upon the quality of data and the robustness of the predictive systems. Issues like data privacy, the potential for bias in AI algorithms, and the integration of AI recommendations into present healthcare practices should be addressed to fully identify the potential of AI in preventive healthcare. By concentrating on these facets, this study contributes to the understanding of how AI may be used to increase preventive healthcare systems, proposing insights into both the opportunities and challenges that contribute to AI-driven multi-objective decision-making in this significant domain of healthcare [89]. Moreover, preventive healthcare control ML techniques increase preventive care mechanisms. These models leverage complex algorithms to assess extensive datasets, integrating criteria like patient history, genetic predispositions, and environmental variables to recognize persons at risk of special health circumstances. By anticipating potential health risks, preventive healthcare models allow proactive interventions, individualized health recommendations, and determined preventive measures. ML contributes to the frequent refinement of risk evaluation models, facilitating these models to adapt and change according to advent health patterns and personal health profiles [90]. The benefits include early recognition of health challenges, personal preventive treatment regimens, and the probability of considerable healthcare cost savings by declining the impact of preventive diseases. In addition, preventive healthcare models, which are strengthened by ML methodologies in multi-objective healthcare management, serve a key part in forecasting and harnessing health issues before they worsen. These models leverage predictive analytics to recognize persons who are at high risk of getting certain health illnesses according to their past health data, genetic predispositions, and lifestyle selection. They may leverage ML algorithms to assess huge datasets and recognize trends and patterns relevant to possible health risks. The applications contain individualized health suggestions, determined interventions, and early recognition methods [72]. Preventive healthcare systems aid in dropping disease incidence, optimize resource utilization, and facilitate a preventive healthcare trend, allowing better public health findings. The downsides and drawbacks of ML-based ISs contain different criteria that affect their productivity. One primary issue is the interpretability of the ML technique, since sophisticated algorithms can lack transparency, making it challenging for healthcare providers to rely on the decision-making procedure. On the other hand, historical data may contain biases, potentially propelling incorrect projections or exacerbating recent healthcare inequities [85].