Numerical-Symbolic Learning from Biomedical Data

Learning from small datasets is crucial in biomedical research due to the limited availability of large, annotated data in many domains. Inductive Logic Programming (ILP) offers a robust framework for integrating symbolic reasoning with machine learning, enabling the generation of interpretable models. In this work, we explore the application of numerical symbolic learning approaches to biomedical data using ILP systems such as NumLog, PyGol, and NumSynth. These systems demonstrate superior efficiency in handling numerical features and extracting meaningful rules compared to traditional rule learning and machine learning methods. We evaluate these approaches on two datasets: a neurodegenerative dataset for Alzheimer's disease detection from fundus images and the benchmark Breast Cancer dataset. The results underscore the potential of ILP-based numerical-symbolic learning in identifying complex relationships within biomedical data, providing actionable insights for advancing precision medicine and disease diagnosis.