Intelligent decision support systems (IDSS) for complex healthcare applications investigate the massive amount of complex medical data to help physicians, academicians, pathologists, doctors and other healthcare professionals. A decision support system (DSS) is an intelligent system, which offers excellent assistance in diverse levels of health-related disease diagnosis. Important data is added on a uniform basis, making it a dynamic information model. Internet of Things, embedded devices, sensors, mobile applications, manual data entry and online sources are examples of complex data sources for IDSS. The data supported by IDSS considerably aids in the early diagnosis of diseases and corresponding treatments. Intelligent DSS makes use of artificial intelligence (AI) techniques to improvise the process of complex decision-making.

AI tools such as metaheuristics, fuzzy logic, case-based reasoning, artificial neural networks, and intelligent agents can be integrated with DSS for healthcare diagnosis. Metaheuristic optimization algorithms can handle real-world applications such as machine learning, artificial intelligence, data mining, data analysis, image processing, and more. Those algorithms are developed from the behavior of birds, animals, insects, or from any specific characteristics. To reduce the complexity of research work, algorithms have recently been used for prediction, identification, classification, and detection of diseases via various analysis tools.

This Special Issue focuses on the development of the latest and advanced metaheuristic algorithms for intelligent DSS in complex healthcare applications. It serves as a platform for dissemination as well as sharing the latest scientific contributions from metaheuristic algorithms. We invite authors to contribute original research articles as well as review articles on recent advances in these active research areas.

Potential topics include but are not limited to the following:

• Advances in metaheuristic optimization algorithms based IDSS for complex disease prediction methods and techniques
• Advances in metaheuristic optimization algorithms based IDSS for complex data mining and knowledge discovery algorithms
• Intelligent decision making systems for computer-aided diagnostic systems
• Advances in swarm intelligence-based IDSS models for complex healthcare applications
• Advances in nature-inspired metaheuristic optimization based IDSS models for complex healthcare applications
• Advances in metaheuristic optimization algorithms based IDSS for big data analytics in healthcare and rehabilitation
• Advances in metaheuristic based clinical imaging techniques for complex disease diagnosis
• Collection, integration, and analysis of complex clinical data using machine learning techniques with advanced metaheuristic algorithms
• Modified or improved metaheuristic optimization based IDSS models for complex healthcare applications
• New metaheuristic optimization based IDSS models for complex healthcare applications
• Advances in hybrid metaheuristic optimization based IDSS models for complex healthcare applications