In the medical field, efficient and accurate classification of sequential and structured data is crucially important and useful for early diagnosis and treatment. Traditional machine learning models struggle with the complexity and nonlinearity of dynamic datasets, whereas deep learning models, despite their effectiveness, require extensive resources and lack transparency. This paper proposes a novel lightweight ensemble framework integrating a parameterized SoftMax function with a non-parametric Random Forest method through a soft voting mechanism, supported by the Nonlinear AutoRegressive eXogenous (NARX) model and optimized using a forward orthogonal search and selection (FOSS) algorithm... More >