## 3 Related Works  
In the previous section, we discussed fundamental concepts of ML-powered ISs and healthcare management. This part explores several related papers conducted currently. Investigating related previous works on ML methods within this context is crucial for several reasons. First, it allows researchers to build upon existing knowledge and methodologies, fostering the evolution of more robust and effective ML solutions tailored to the complexities of healthcare management. Understanding prior studies provides insights into successful approaches, pitfalls, and emerging trends, guiding the development of innovative models that can address multiple objectives concurrently in healthcare settings. Furthermore, a thorough review of related works helps to identify gaps in the current research, informing future directions for ML applications in healthcare management systems and, ultimately, contributing to the advancement of intelligent systems that can improve decision-making processes, resource allocation, and patient outcomes in a multi-objective healthcare environment. By the same token, [PERSON] [33] explored the integration of AI and deep-learning (DL) techniques into cloud computing to enhance operational efficiency. The focus is on leveraging these technologies to improve various aspects of cloud computing, such as resource allocation, task scheduling, and overall system performance. This paper discussed the potential benefits, challenges, and practical considerations associated with the integration of AI and DL in cloud computing environments. However, specific details and findings are not provided in the request, so a more detailed summary would require access to the paper's content. Also, [PERSON], [PERSON] [34] conducted a comparative analysis between traditional ML methods and DL approaches in the context of computer-aided diagnosis (CAD) systems. This study evaluated the performance, advantages, and limitations of both types of techniques in medical diagnosis applications. The comparison encompassed factors such as accuracy, efficiency, and generalizability, providing insights into the strengths and weaknesses of classical ML and DL approaches in the specific domain of computer-aided medical diagnosis systems. Moreover, [PERSON], [PERSON] [35] presented a holistic survey and evaluation of the multi-objective optimization on the foundation of ratio analysis (MOORA) technique in the field of decision-making. MOORA is a method applied for addressing multi-objective optimization issues by assessing alternatives according to a ratio analysis technique. This article enveloped the essential applications and variations of the MOORA strategy in decision-making procedures. It stated the upsides and drawbacks of the technique and proposed visions for its efficiency in diverse fields. In addition, [PERSON], [PERSON] [36] explored the application of DL techniques in healthcare systems with a focus on improving the quality of service. This paper stated how DL methods are employed to enhance various aspects of healthcare services, such as diagnostics, treatment planning, and patient care. It addressed the advantages and challenges of integrating DL into healthcare systems and may provide insights into how these technologies contribute to optimizing the quality of healthcare delivery. Furthermore, [PERSON] [37] conducted a systematic review of the applications of multi-objective optimization in the context of reverse logistics. The review encompassed various studies and applications that are related to optimizing multiple conflicting objectives in reverse logistics processes, considering factors such as cost, environmental impact, and efficiency. The authors summarized the key findings, trends, and challenges identified in the reviewed literature, providing insights into the state of research and applications in the field. Table 1 indicates previous related works and a comparison between them and our work.