AdaBoost-Based Classification for Bone Sarcoma Outcome Predic-tion: A Comparative Machine Learning Approach

Abstract

Bone sarcomas represent aggressive malignancies with complex clinical presentations where traditional prognostic methods often lack precision in predicting patient outcomes. This study developed and validated machine learning models for predicting bone sarcoma patient outcomes using a comprehensive dataset from Memorial Sloan Kettering Cancer Center spanning 2010-2020, comparing multiple algorithms including AdaBoost, Logistic Regression, Ridge Classifier, Quadratic Discriminant Analysis, and Linear Discriminant Analysis. AdaBoost demonstrated superior performance with 0.84 accuracy, 0.875 sensitivity, 0.7955 specificity, 0.8448 precision, 0.8333 negative predictive value, and 0.8596 F1 score, outperforming other algorithms which achieved 0.83 accuracy and 0.8496 F1 score. Statistical analysis confirmed significant differences between classifiers with F-statistic of 21.9130 and p-value less than 0.0001. The study concludes that AdaBoost-based classification provides a reliable framework for bone sarcoma outcome prediction with superior performance, demonstrating potential for clinical integration to support treatment planning and establishing a foundation for precision medicine applications in orthopedic oncology.