Comprehensive Analysis of Cholesteatoma in Chronic Otitis Media: Integrating Traditional Statistical Methods with Machine Learning Approaches

Background: Cholesteatoma detection in chronic otitis media (COM) remains challenging, requiring comprehensive diagnostic approaches. While traditional statistical analysis of individual biomarkers may show limitations, the integration of multiple analytical methods including machine learning can provide deeper insights into disease prediction. This study aimed to conduct a comprehensive analysis combining traditional statistical methods with advanced machine learning to evaluate cholesteatoma detection using hematological parameters. Methods: A retrospective analysis was conducted on 79 patients with COM (40 with cholesteatoma, 39 without cholesteatoma). Complete blood count parameters and inflammatory ratios (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], monocyte-tolymphocyte ratio [MLR]) were analyzed using (1) traditional statistical methods including Mann–Whitney U-tests, receiver oeprating characteristic (ROC) curve analysis, and correlation studies and (2) advanced machine learning algorithms including XGBoost and logistic regression with cross-validation. Results: Traditional statistical analysis revealed important baseline characteristics: no significant age differences between groups, and individual inflammatory ratios showed limited discriminative ability (NLR: P = .9414, PLR: P = .6067, MLR: P > .05). However, machine learning integration demonstrated that these same parameters could achieve clinically relevant predictive capability. XGBoost achieved 66.7% accuracy with 0.701 AUC-ROC, while ROC analysis of individual parameters yielded area under the receiver operating characteristic curve (AUC) values of 0.508- 0.545. The combination of statistical validation and machine learning optimization provided complementary insights. Conclusion: This comprehensive analysis demonstrates the value of integrating traditional statistical methods with machine learning approaches. While individual biomarker analysis confirmed the complexity of cholesteatoma diagnosis, machine learning successfully leveraged multiple parameters to achieve predictive capability. The complementary use of both analytical approaches provides a robust foundation for clinical decision-making and establishes a methodology for future diagnostic tool development.