Normalized matrix.

Infectious diseases wield significant influence on global mortality rates, largely due to the challenge of gauging their severity owing to diverse symptomatology. Each nation grapples with its unique obstacles in combatting these diseases. This study delves into three distinct decision-making methodologies for medical diagnostics employing Neutrosophic Hypersoft Set (NHSS) and Plithogenic Hypersoft Set (PHSS), extensions of the Hypersoft set. It introduces state-of-the-art AI-driven techniques to enhance the precision of medical diagnostics through the analysis of medical imagery. By transforming these images into the aforementioned sets, the analysis becomes more refined, facilitating more accurate diagnoses. The study advocates various courses of action, including isolation, home or specialized center quarantine, or hospitalization for further treatment. The novelty in this study utilizes cutting-edge AI methods to enhance medical imaging, transforming them into accurate diagnostic tools, marking a significant change in how infectious diseases are addressed. By combining machine learning and pattern recognition, it offers the potential to overhaul healthcare worldwide, facilitating accurate diagnoses and customized treatment plans, ultimately reducing the global burden of infectious diseases on mortality rates.

传染病对全球死亡率影响显著，其核心难点在于症状表现多样，难以精准评估病情严重程度。各国在抗击此类传染病的过程中，均面临各自独特的挑战。本研究针对医疗诊断场景，探索了三种差异化决策方法，所采用的中智超软集（Neutrosophic Hypersoft Set, NHSS）与聚合超软集（Plithogenic Hypersoft Set, PHSS），均为超软集（Hypersoft Set）的扩展模型。本研究引入当前前沿的人工智能驱动技术，通过医学影像分析提升医疗诊断的精准度。通过将医学影像转换为上述模型，可使分析过程更为精细，助力实现更精准的诊断结果。本研究提出了多种干预方案，包括隔离、居家或专门医疗机构的检疫观察，以及入院接受进一步治疗。本研究的创新之处在于，依托前沿人工智能技术优化医学影像分析，将其转化为精准的诊断工具，为传染病防控思路带来重大变革。本研究结合机器学习与模式识别技术，有望革新全球医疗体系，助力实现精准诊断与个性化治疗方案，最终减轻传染病对全球死亡率的负担。