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

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.