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Class imbalance and the presence of irrelevant or redundant features in training data can pose serious challenges to the development of a classification framework. This paper proposes a framework for developing a Clinical Decision Support System (CDSS) that addresses class imbalance and the feature selection problem. Under this framework, the dataset is balanced at the data level and a wrapper approach is used to perform feature selection. The following three clinical datasets from the University of California Irvine (UCI) machine learning repository were used for experimentation: the Indian Liver Patient Dataset (ILPD), the Thoracic Surgery Dataset (TSD) and the Pima Indian Diabetes (PID) dataset. The Synthetic Minority Over-sampling Technique (SMOTE), which was enhanced using Orchard's algorithm, was used to balance the datasets. A wrapper approach that uses Chaotic Multi-Verse Optimisation …