Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.

IF 3.4 3区 医学 Q2 INFECTIOUS DISEASES
Gizachew Mulu Setegn, Belayneh Endalamaw Dejene
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引用次数: 0

Abstract

Background: Monkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemic regions in Central and, West Africa and the world. The disease transmits through contact with infected animals and humans, leading to fever, rash, and lymphadenopathy symptoms. Control efforts include surveillance, contact tracing, and vaccination campaigns; however, the increasing number of cases underscores the necessity for a coordinated global response to mitigate its impact. Since monkeypox has become a public health issue, new methods for efficiently identifying cases are required. The control of monkeypox infections depends on early detection and prediction. This study aimed to utilize Symptom-Based Detection of Monkeypox using a machine-learning approach.

Methods: This research presents a machine learning approach that integrates various Explainable Artificial Intelligence (XAI) to enhance the detection of monkeypox cases based on clinical symptoms, addressing the limitations of image-based diagnostic systems. In this study, we used a publicly available dataset from GitHub containing clinical features about monkeypox disease. The data have been analysed using Random Forest, Bagging, Gradient Boosting, CatBoost, XGBoost, and LGBMClassifier to develop a robust predictive model.

Results: The study shows that machine learning models can accurately diagnose monkeypox based on symptoms like fever, rash, lymphadenopathy and other clinical symptoms. By using XAI techniques for feature importance, the approach not only achieved high accuracy but also provided transparency in decision-making. This integration of explainable Artificial intelligence (AI) enhances trust and allows healthcare professionals to understand predictions, leading to timely interventions and improved public health responses to monkeypox outbreaks. All Machine learning methods have been compared with the evaluation matrix. The best performance was for the LGBMClassifier, with an accuracy of 89.3%. In addition, multiple Explainable Techniques tools were used to help in examining and explaining the output of the LGBMClassifier model.

Conclusions: Our research shows that combining explainable techniques with AI models greatly enhances the accuracy of case detection and boosts the trust of medical professionals. These models result in directly involving the reader and health care professional in the decision-making process, making informed decisions, and efficiently allocating resources by providing insight into the decision-making process. In addition, this study underscores the potential of AI in public health surveillance, particularly in enhancing responses to emerging infectious diseases such as monkeypox.

基于猴痘症状的可解释AI检测:一种机器学习方法。
背景:猴痘是一种病毒性人畜共患疾病,是一个新兴的全球卫生问题,其发病率和疫情不断上升,已超出中非、西非和世界的流行区域。该病通过与受感染的动物和人接触传播,导致发烧、皮疹和淋巴结病症状。控制工作包括监测、接触者追踪和疫苗接种运动;然而,越来越多的病例凸显了采取协调一致的全球应对措施以减轻其影响的必要性。由于猴痘已成为一个公共卫生问题,因此需要有效识别病例的新方法。猴痘感染的控制取决于早期发现和预测。本研究旨在利用机器学习方法利用基于症状的猴痘检测。方法:本研究提出了一种整合各种可解释人工智能(XAI)的机器学习方法,以增强基于临床症状的猴痘病例检测,解决基于图像的诊断系统的局限性。在这项研究中,我们使用了一个来自GitHub的公开数据集,其中包含猴痘疾病的临床特征。使用随机森林、Bagging、Gradient Boosting、CatBoost、XGBoost和LGBMClassifier对数据进行分析,以开发一个鲁棒的预测模型。结果:研究表明,机器学习模型可以根据发热、皮疹、淋巴结病等临床症状准确诊断猴痘。通过使用XAI技术对特征重要性进行处理,不仅达到了较高的准确率,而且为决策提供了透明度。这种可解释的人工智能(AI)的整合增强了信任,使医疗保健专业人员能够理解预测,从而及时干预并改善公共卫生对猴痘爆发的反应。所有的机器学习方法都与评价矩阵进行了比较。其中,LGBMClassifier的准确率最高,达到89.3%。此外,还使用了多个Explainable Techniques工具来帮助检查和解释LGBMClassifier模型的输出。结论:我们的研究表明,将可解释技术与人工智能模型相结合,大大提高了病例检测的准确性,提高了医疗专业人员的信任度。这些模型使读者和医疗保健专业人员直接参与决策过程,做出明智的决策,并通过提供对决策过程的洞察,有效地分配资源。此外,这项研究强调了人工智能在公共卫生监测方面的潜力,特别是在加强对猴痘等新发传染病的反应方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Infectious Diseases
BMC Infectious Diseases 医学-传染病学
CiteScore
6.50
自引率
0.00%
发文量
860
审稿时长
3.3 months
期刊介绍: BMC Infectious Diseases is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of infectious and sexually transmitted diseases in humans, as well as related molecular genetics, pathophysiology, and epidemiology.
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