## 2 Basic Concepts and Corresponding Terminology  
In the previous part, we looked into the importance of ML-based ISs, multi-objective healthcare management, and its applications. This section is dedicated to fundamental concepts and associated terminology; the term \"ISs\" pertains to sophisticated technological frameworks. These frameworks integrate artificial intelligence, ML, and information-processing capabilities to decipher and analyze intricate datasets. Engineered to independently achieve, process, and interpret information, these models frequently adapt and increase their function [20]. Leveraging algorithms and computational models, ISs derive meaningful perspectives, allow decision-making procedures, and optimize tasks through various areas. Their total objective is to enhance effectively, present significant visions, and guide well-informed decision-making in dynamic and data-enriched fields like healthcare, finance, and logistics [21]. Healthcare management includes the coordination, planning, and implementing of activities in healthcare organizations to guarantee the productive delivery of medical services [22]. It contains a wide range of responsibilities, consisting of strategic planning, financial management, resource allocation, and personnel coordination [23]. Healthcare managers are tasked with optimizing functional efficiency, developing patient care, and addressing the complications of healthcare models. Their part broadens to overseeing administrative performances, implementing roles, and promoting a culture of quality and patient security across healthcare institutions. Successful healthcare management is essential for maintaining the balance between facilitating high-quality care, controlling costs, and meeting the progressed demands of patients and communities [24]. ML is a subject of AI that emphasizes the improvement of strategies and systems that enable computer systems to learn trends and make forecasts or evaluations without explicit programming [25]. It implies leveraging data to train these algorithms, facilitating them to continuously develop their function and adapt to altering inputs. ML algorithms might be categorized into three types, namely supervised learning, unsupervised learning, and reinforcement learning, each adapted to specialized learning scenarios [26]. This method finds applications in many areas, like image and audio recognition, natural language processing, and data analytics, contributing to job automation and deriving related perspectives from complex datasets [27]. Multi-objective healthcare management refers to a holistic method in healthcare administration that simultaneously solves and optimizes diverse interrelated goals to gain comprehensive and sustainable results [28]. This strategic management system includes various objectives like developing patient findings, increasing performance effectiveness, managing costs, ensuring resource allocation, and prioritizing preventive measures. Contrary to single-emphasized methods, multi-objective healthcare management identifies the interdependence of these objectives and aims to explore the synergies that benefit the total healthcare model [29]. By balancing evolving goals, this method enables building a resilient and adaptive healthcare system capable of delivering high-quality care, fostering patient satisfaction, and responding effectively to evolving challenges from the healthcare perspective [30]. ML-powered ISs leveraged for multi-objective healthcare management include a scope of algorithms modeled to evaluate complicated healthcare data, extract patterns, and inform decision-making procedures [31]. Supervised-learning methods, like classification and regression, are associated with predictive modeling for patient findings, resource allocation, and cost-effectiveness analysis. Unsupervised-learning techniques, containing clustering and dimensionality reduction, allow for detecting patterns across healthcare datasets.