Robust cancer crowdfunding predictions: Leveraging large language models and machine learning for success analysis

作者：Abhishikta Roy, Vineet Srivastava, Lokesh Boggavarapu, Ranganathan Chandrasekharan, Edward Mensah, John P. Galvin, Runa Bhaumik · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.02.24.25322692 · 研究领域：FinTech, Crowdfunding, Digital Finance、Artificial Intelligence in Healthcare and Education

Abstract Background In the field of medical crowdfunding prediction, traditional statistical methods have long been the standard. Machine learning algorithms are popular because they can model complex relationships between variables, capture interactions, and provide more accurate predictions, even when input variables are highly correlated. Furthermore, previous research has largely overlooked the quantitative assessment of success levels and the selection of key predictors. To address these limitations, a novel approach is needed that leverages advanced machine learning techniques. Objective This study aimed to address these gaps by proposing a robust feature engineering approach that leverages the capabilities of large language models (LLMs). The goal was to extract the success determinants using a large language model for a cancer crowdfunding campaign. Furthermore, this study evaluated the performance of four machine learning algorithms in predicting campaign success and quantitatively assessed the level of success. Method We separately analyzed linguistic and social determinants of health features to understand how much each factor contributes to a crowdfunding campaign’s success. These features were generated using a large language model (GPT-4o). A random forest algorithm with a permutation technique was used to rank the features. We comparatively evaluated the prediction accuracy, sensitivity, and specificity of four machine learning algorithms, random forest, gradie...