Enhancing Decision-Making for Imbalanced Medical Datasets Using BDDs and Low-Code/No-Code

Imbalanced datasets pose a challenge wherever accurate predictions are essential. This paper explores using low-code/no-code platforms, such as Pyrus and ADD-Lib, to apply data resampling techniques and binary decision diagrams for more accessible and reliable ML workflows. Tested on three medical datasets, these techniques improve model performance by addressing class imbalances. The integration of resampling and formal methods enhances prediction accuracy while making ML tools more accessible to professionals, enabling better decision-making in critical applications. The techniques are applicable to any domain, not just in healthcare.