Fuzzy Decision-Making Framework for Sensitively Prioritizing Autism Patients with Moderate Emergency Level

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that requires careful assessment and management. The prioritization of ASD patients involves navigating through complexities such as conflicts, trade-offs, and the importance of different criteria. Therefore, this study focuses on prioritizing patients with ASD in the healthcare setting through an evaluation and benchmarking framework. The aim of this study is to develop a framework that utilizes Multi-Criteria Decision Making (MCDM) methods to assist healthcare professionals in prioritizing ASD patients, particularly those with moderate injury levels. The methodology of the framework outlines several phases, including dataset identification, development of a decision matrix, weighting of 19 ASD criteria using the FWZIC method, ranking 432 patients using the VIKOR method, and evaluating the proposed framework using four sensitivity analysis scenarios. Among the 19 ASD criteria, the criterion 'verbal communication' obtained the highest weight. Additionally, criteria such as 'laughing for no reason', 'nodding', 'patient movement at home', and 'pointing with the index finger' obtained similar higher weights, indicating their potential impact on ASD patients. The experimental results highlight the significance of adjusting ASD weights in influencing the final rankings obtained through the VIKOR method. This emphasizes the need for careful consideration when assigning weights to the 19 ASD criteria to ensure accurate prioritization. Moreover, the framework provides valuable insights into improving the care and support provided to individuals with autism in Iraq. The findings contribute to the existing body of knowledge in the field of autism care prioritization and pave the way for future research and interventions aimed at enhancing the quality of care for individuals with autism in Iraq.

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

“…Feature extraction is one of the essential techniques in computer vision and image processing. This technique is vital to identifying relevant patterns and structures within the image [26]- [28]. So that the extracted features are meaningful in order to reduce the complexity of the data and thus be more amenable to analysis and decision-making tasks.…”