Is Deep Learning Better than Machine Learning to Predict Benign Laryngeal Disorders?

It is important in otolaryngology to accurately understand the etiology of a laryngeal disorder, diagnose it early, and provide appropriate treatment accordingly. The objectives of this study were to develop models for predicting benign laryngeal mucosal disorders based on deep learning, naive Bayes model, generalized linear model, a Classification and Regression Tree (CART), and random forest using laryngeal mucosal disorder data obtained from a national survey and confirm the best classifier for predicting benign laryngeal mucosal disorders by comparing the prediction performance and runtime of the developed models. This study analyzed 626 subjects (313 people with a laryngeal disorder and 313 people without a laryngeal disorder). In this study, deep learning was the best model with the highest accuracy (0.84). However, the runtime of deep learning was 39min 41sec, which was a 10 times longer development time than CART (3min 7sec). This model confirmed that subjective voice problem recognition, pain and discomfort in the last two weeks, education level, occupation, mean monthly household income, high-risk drinker, and current smoker were major variables with high weight for the benign laryngeal mucosal disorders of Korean adults. Among them, subjective voice problem recognition was the most important factor with the highest weight. The results of this study implied that the prediction performance of deep learning could be better than that of machine learning for structured data, such as health behavior and demographic factors as well as video and image data.

@article{Byeon2021,
  title     = {Is Deep Learning Better than Machine Learning to Predict Benign Laryngeal Disorders?},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {4},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Haewon Byeon},
  doi       = {10.14569/IJACSA.2021.0120415},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120415}
}