Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania.
PLoS One. 2024 Oct 9;19(10):e0307437. doi: 10.1371/journal.pone.0307437. eCollection 2024.
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有重大影响,主要是因为由于症状的多样性,难以衡量其严重程度。每个国家在对抗这些疾病时都面临着自己独特的障碍。本研究探讨了使用 Neutrosophic Hypersoft Set (NHSS) 和 Plithogenic Hypersoft Set (PHSS) 的三种不同的医学诊断决策方法,这两种方法都是 Hypersoft 集的扩展。本研究引入了最先进的人工智能驱动技术,通过分析医学图像来提高医学诊断的精度。通过将这些图像转换为上述集合,分析变得更加精细,从而更准确地进行诊断。本研究主张采取各种行动方案,包括隔离、在家或专门中心检疫,或住院进一步治疗。本研究的新颖之处在于利用最先进的人工智能方法来增强医学图像,将其转化为准确的诊断工具,这标志着传染病处理方式的重大改变。通过结合机器学习和模式识别,它有可能彻底改变全球医疗保健,实现准确的诊断和定制的治疗方案,最终降低传染病对全球死亡率的影响。