Personal and Clinical Predictors of Voice Therapy Outcomes: A Machine Learning Analysis Using the Voice Handicap Index

Abstract

In this study, we examine the predictive factors influencing the outcomes of voice treatment in patients with voice-related disorders, using the voice handicap index (VHI) as a key assessment tool. By analyzing various personal habits and clinical variables, we identify the primary factors associated with changes when comparing VHI scores before and after voice treatment. For this research, we employed binomial logistic regression, random forest (RF), and a multilayer perceptron (MLP) model to evaluate the effectiveness of voice treatment. The findings reveal that gender (with female patients showing greater improvements in VHI scores compared to male patients), surgical history, voice use status, and voice training status are significant predictors of therapy outcomes. The MLP model demonstrated high accuracy, sensitivity, and specificity, with an area under the curve (AUC) value of 0.87 indicating its potential as a valuable clinical predictive tool; however, the model’s relatively low specificity suggests the need for further refinement to enhance its predictive accuracy. The results of this study provide valuable insights for clinicians and speech–language pathologists in developing personalized treatment strategies to optimize the effectiveness of voice therapy. Future research should prioritize the validation of these findings in larger and more diverse population samples. Furthermore, it is essential to explore additional predictive variables in order to enhance the model’s accuracy across different types of voice disorders.

Keywords

• voice handicap index
• predictive factors
• voice therapy
• voice disorders
• machine learning
• regression analysis