Special Issue "Intelligent Decision Support Systems for Complex Healthcare Applications"

Submission Deadline: 31 January 2021 (closed)
Guest Editors
Dr. K. Shankar, Alagappa University, India.
Dr. Gyanendra Prasad Joshi, Sejong University, South Korea.
Dr. Gia Nhu Nguyena, Duy Tan University, Viet Nam.


Intelligent decision support system (IDSS) for Complex healthcare applications investigates the massive quantity of complex medical data to help physicians, academicians, pathologists, doctors and other healthcare professionals. Decision support system (DSS) is an intelligent system, which offers excellent assistant in diverse levels of health-related disease diagnosis. Besides, it is a dynamic information model as important data is added on a uniform basis. Internet of Things, embedded devices, sensors, mobile applications, manual data entry and online sources are few complex data sources for IDSS. The data supported by IDSS considerably aid in early diagnosis of diseases and corresponding treatments. Intelligent DSS make use of artificial intelligence (AI) techniques to improvise the process of complex making decisions. AI tools such as Metaheuristic, Fuzzy Logic, Case based Reasoning, Artificial Neural Networks, and Intelligent Agents can be integrated to DSS for healthcare diagnosis.

Meta-heuristics optimization algorithm can handle real-world application such as machine learning, artificial intelligence, data mining, data analysis, image processing etc. Those algorithms are developed from the behavior of birds, animals, insects, or from any specific characteristics. To reduce the complexity of research work, recently algorithms are used for the purpose of prediction, identification, classification, and detection of diseases via various analysis tools. This special issue focuses on the development of latest and advanced metaheuristic algorithms for intelligent DSS in complex healthcare applications. It serves as a platform for dissemination as well as sharing of 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.

Topics of interest include, but are not limited to:

• 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 system.

• 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 healthcare and rehabilitation data analytics.

• 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.

Advances in Metaheuristic Optimization Algorithms, Intelligent Decision Support Systems, Complex Data mining and knowledge discovery algorithms, Computer-aided diagnostic system, Complex healthcare applications, Complex healthcare applications, Big healthcare and rehabilitation data analytics, Machine learning techniques, Deep Learning

Published Papers
  • Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things
  • Abstract The scope of the Internet of Things (IoT) applications varies from strategic applications, such as smart grids, smart transportation, smart security, and smart healthcare, to industrial applications such as smart manufacturing, smart logistics, smart banking, and smart insurance. In the advancement of the IoT, connected devices become smart and intelligent with the help of sensors and actuators. However, issues and challenges need to be addressed regarding the data reliability and protection for significant next-generation IoT applications like smart healthcare. For these next-generation applications, there is a requirement for far-reaching privacy and security in the IoT. Recently, blockchain systems have emerged… More
  •   Views:162       Downloads:161        Download PDF

  • Computer Decision Support System for Skin Cancer Localization and Classification
  • Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More
  •   Views:150       Downloads:103        Download PDF

  • Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier
  • Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More
  •   Views:225       Downloads:170        Download PDF

  • Medical Diagnosis Using Machine Learning: A Statistical Review
  • Abstract Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry… More
  •   Views:539       Downloads:384        Download PDF

  • Blockchain-Enabled EHR Framework for Internet of Medical Things
  • Abstract The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for healthcare services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying commination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most healthcare services within… More
  •   Views:489       Downloads:285        Download PDF

  • Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data
  • Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More
  •   Views:464       Downloads:307        Download PDF

  • A New Decision-Making Model Based on Plithogenic Set for Supplier Selection
  • Abstract Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability. The choice of supplier is a multi-criteria decision making (MCDM) to obtain the optimal decision based on a group of criteria. The health care sector faces several types of problems, and one of the most important is selecting an appropriate supplier that fits the desired performance level. The development of service/product quality in health care facilities in a country will improve the quality of the life of its population. This paper proposes an integrated multi-attribute border approximation area comparison (MABAC) based on… More
  •   Views:527       Downloads:274        Download PDF

  • Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques
  • Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More
  •   Views:569       Downloads:336        Download PDF

  • Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model
  • Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More
  •   Views:413       Downloads:267        Download PDF

  • An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy
  • Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More
  •   Views:449       Downloads:281        Download PDF

  • Fog-Based Secure Framework for Personal Health Records Systems
  • Abstract The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’ repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Assuming a massive demand of PHR data within… More
  •   Views:532       Downloads:358        Download PDF

  • Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT
  • Abstract Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents… More
  •   Views:557       Downloads:385        Download PDF

  • Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications
  • Abstract Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer… More
  •   Views:922       Downloads:477        Download PDF

  • Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things
  • Abstract Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with… More
  •   Views:779       Downloads:417        Download PDF