OPTIMIZED SYMPTOM-BASED DEEP LEARNING FRAMEWORK FOR MONKEYPOX DIAGNOSIS WITH LIME EXPLAINABILITY

Monkeypox is an emerging zoonotic disease that has raised global health concerns due to its increasing transmission rates. Traditional diagnostic methods rely on laboratory testing, which can be timeconsuming and inaccessible in resource-limited settings. This study presents an Optimized Deep Neural Framework (ODNF) to diagnose monkeypox based on clinical symptoms, leveraging deep learning for accurate and rapid classification. The research explores various machine learning models, including Random Forest, XG Boost, and Cat Boost, before implementing ODNF, which achieved superior performance with a 99% accuracy rate. The dataset underwent preprocessing steps, including handling imbalanced data and feature encoding, ensuring optimal learning. Additionally, Local Interpretable Model-Agnostic Explanations (LIME) was employed to enhance model interpretability, providing insights into symptom-based predictions. Comparative evaluation against traditional models demonstrated that ODNF outperforms existing approaches, making it a viable AI-based diagnostic tool for monkeypox detection.