Machine Learning in Predicting the Success of Spine Surgery: A Multivariable Study

This study explores the application of Artificial Intelligence (AI) in spine surgery, with a focus on enhancing precision and accuracy in outcome prediction. Leveraging machine learning (ML) models - including GaussianNB, ComplementNB, KNN, and Decision Trees - we analyze a rich dataset derived from 244 spine surgery patients. This dataset comprises 24 diverse variables, capturing elements such as pre-surgical conditions, socioeconomic status, psychometric evaluations, and various analytical metrics. Notably, one critical variable is the surgery's success, serving as the primary outcome for prediction. The data was meticulously categorized into seven distinct groups, reflecting various aspects of the surgical process and patient backgrounds. This structured approach enabled a targeted and nuanced analysis, deepening our understanding of the key factors instrumental in predicting surgical outcomes. We employed a stratified split methodology for our dataset, dedicating 80% to training and 20% to testing. This was supplemented by 5-fold cross-validation and an extensive grid search optimization for refining the KNN and Decision Trees models. Our results underscore the profound capability of AI in predicting the outcomes of spine surgeries. The KNN model showed remarkable proficiency, particularly in analyzing groups defined by pre-surgical and analytical variables, demonstrating its prowess in handling complex medical datasets. This study not only evidences the effectiveness of specific ML models in medical prognostics but also highlights AI's transformative potential in healthcare. It underlines the critical role of AI in advancing medical diagnostics and decision-making for surgeries that entail multifaceted data analysis. These insights pave the way for future research into the broader application of AI in medicine, promising more personalized and effective treatment strategies and effective treatment approaches.