Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges

Title

Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges

Creator

Olakkengil, Divya Mathews; Aurelia, Sagaya P.

Description

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individuals quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. 2025 by the authors.

Source

Engineering Proceedings;Volume;107;Issue;1;Article No.;28;

Date

01-01-2025

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

Subject

logistic regression; machine learning; voice pathology

Coverage

Olakkengil D.M., Department of Computer Science, CHRIST University, Bangalore, 560029, India, Department of Computer Science, Vimala College (Autonomous), Thrissur, 680009, India; Aurelia S.P., Department of Computer Science, CHRIST University, Bangalore, 560029, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 26734591;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.3390/engproc2025107028

https://www.scopus.com/pages/publications/105025653560?origin=resultslist