Application of multi-objective decision-making based on entropy weight-TOPSIS method and RSR method in the analysis of orthopedic disease - Deyu An; Nan Wang (Discover Artificial Intelligence)
Abstract In the era of medical big data, the analysis of orthopedic disease types demands precision and multi-dimensional insight. Traditional analysis models, limited by their one-dimensional or simplistic structures, struggle to interpret complex clinical data comprehensively. To address this, we innovatively integrate the entropy weight technique with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method and the Rank Sum Ratio (RSR) method. This approach leverages objective weighting based on information entropy and precise solution differentiation from the TOPSIS method to enhance the RSR method's ranking capability while retaining the original quantitative information. Based on a dataset of 100 orthopedic diseases across 18 indicators from five evaluation dimensions, the proposed model identified “lumbar disc herniation with nerve root disease” as the top-ranking disease (comprehensive index, Ci = 0.6915), followed by “lumbar disc herniation with sciatica” (Ci = 0.6860) and “lumbar spinal stenosis” (Ci = 0.6368). The functional positioning dimension contributed the highest indicator weight (58.107%), with the “number of patients undergoing level-four surgeries” being the most influential indicator (21.178%). The RSR regression model demonstrated strong goodness of fit (coefficient of determination, R2 = 0.887; F = 770.543; p < 0.001), confirming the robustness and validity of the analysis. This integrated model provides a reliable and objective decision-making tool for classifying orthopaedic disease types in clinical practice. Graphical abstract