SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator
Olive R. Cawiding, Sieun Lee, Hyeontae Jo, Sungmoon Kim, Sooyeon Aly Suh, Eun Yeon Joo, Seockhoon Chung, Jae Kyoung Kim · Computers in Biology and Medicine · 2024
= 0.82) and achieved AUROC values of 0.85-0.91 for various sleep disorders, closely matching those of SLEEPS (0.88-0.94). By generating accurate and interpretable score tables, SymScore ensures that healthcare professionals can easily explain and trust its results without specialized machine learning knowledge. Thus, SymScore advances explainable AI for healthcare by offering a user-friendly and resource-efficient alternative to machine learning-based questionnaires, supporting improved patient outcomes and workflow efficiency.