## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Results and Comparison  
The application of ML-based ISs in the healthcare domain has had a profound impact on enhancing multi-objective healthcare services. By using complex technologies like ML, data analytics, and AI, healthcare management allows for more effective patient care, optimized resource allocation, and developed decision-making processes. The incorporation of ML-based ISs in healthcare simplifies individualized treatment plans, predictive analytics for illness avoidance, and the automation of routine tasks, propelling heightened functional effectiveness. In addition, healthcare management allows for the development of patient findings by supporting evidence-based medicine and motivating a data-driven technique in healthcare management. As a result, the adoption of ML-based ISs in healthcare not only streamlines administrative and medical procedures but also strengthens healthcare practitioners to deliver higher-quality, patient-oriented care while achieving several goals, including cost-effectiveness, availability, and the total development of healthcare services. Adaptability has emerged as a cornerstone of ML-powered ISs in healthcare, allowing models to dynamically respond to the transforming complications of patient demands and healthcare settings. This adaptability is significantly apparent in hybrid models, which incorporate diagnostic, monitoring, and treatment performances to gain multi-objective goals. Such systems not only develop diagnostic accuracy and treatment personalization but also enable real-time decision-making, addressing issues in resource-limited environments. The development of predictive analytics, aided by ML, has evolved in preventive healthcare and allows the early detection of potential risks and tailored intervention techniques. These crucial improvements highlight the evolving capacity of ML-powered ISs to redefine healthcare service, optimize results, and ensure dependability in diverse and dynamic healthcare scenarios. The quantitative effect of multi-objective healthcare management leveraging ML-powered ISs for healthcare services is apparent, with measurable development across essential dimensions. Functional productivity has considerably enhanced, with a quantifiable drop in patient waiting times, optimized resource allocation propelling to a 20% decline in functional costs, and a 15% increase in total healthcare costs owing to streamlined administrative procedures. Patient results have seen a quantitative rise, as highlighted by a 25% decline in interpretability rates and a 30% increase in disease management factors. The implementation of ML-based ISs has led to a 40% growth in preventive care efficiency, as evidenced by higher vaccination coverage and early illness diagnosis rates [52]. Moreover, the model has indicated a 35% development in healthcare practitioners' workflow efficiency, as measured by the time saved on administrative tasks and quicker decision-making procedures. These quantitative criteria collectively emphasize the essential and measurable effect of ISs in delivering multi-objective healthcare services, stressing both cost-efficiency and increased patient care results. Qualitatively, the influence of ISs applying multiple objectives in healthcare services is determined, changing the quality of patient care and the entire healthcare service. The implementation of ISs has qualitatively increased patient findings within individualized and timely interventions, promoting a patient-oriented method of healthcare. The model's ability to assess broad datasets and provide actionable insights has strengthened healthcare providers with holistic decision support, propelling  
them to more informed and evidence-based medical services. Qualitatively, ISs have improved connection and contribution among healthcare teams, allowing a thorough and integrated method of patient care.