Predicting speech discrimination scores from pure-tone thresholds-A machine learning-based approach using data from 12,697 subjects

• Published in: PloS one Vol. 16; no. 12; p. e0261433
• Main Authors: Kim, Hantai, Park, JaeYeon, Choung, Yun-Hoon, Jang, Jeong Hun, Ko, JeongGil
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.12.2021; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Analysis; Audiometry; Audiometry, Pure-Tone - methods; Auditory discrimination; Auditory system; Auditory Threshold; Biology and Life Sciences; Child; Child, Preschool; Clinical medicine; Computational Biology; Computer and Information Sciences; Data mining; Discrimination Learning; Ears & hearing; Engineering schools; Evaluation; Female; Hearing; Hearing loss; Hearing Loss - physiopathology; Human factors; Humans; Infant; Infant, Newborn; Learning algorithms; Machine Learning; Male; Medicine and Health Sciences; Middle Aged; Models, Statistical; Multilayers; Neural Networks, Computer; Otolaryngology; Patients; People and Places; Physical Sciences; Physicians; Prediction models; Rehabilitation; Reproducibility of Results; Republic of Korea; Research and Analysis Methods; Social Sciences; Speech; Speech Perception; Speech Reception Threshold Test; Support vector machines; Thresholds; Young Adult; Republic of Korea; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0261433

Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately measuring hearing loss conditions is very important for proper patient understanding and treatment. In current-day practice, to quantify the level of hearing loss, physicians exploit specialized test scores such as the pure-tone audiometry (PTA) thresholds and speech discrimination scores (SDS) as quantitative metrics in examining a patient's auditory function. However, given that these metrics can be easily affected by various human factors, which includes intentional (or accidental) patient intervention, there are needs to cross validate the accuracy of each metric. By understanding a "normal" relationship between the SDS and PTA, physicians can reveal the need for re-testing, additional testing in different dimensions, and also potential malingering cases. For this purpose, in this work, we propose a prediction model for estimating the SDS of a patient by using PTA thresholds via a Random Forest-based machine learning approach to overcome the limitations of the conventional statistical (or even manual) methods. For designing and evaluating the Random Forest-based prediction model, we collected a large-scale dataset from 12,697 subjects, and report a SDS level prediction accuracy of 95.05% and 96.64% for the left and right ears, respectively. We also present comparisons with other widely-used machine learning algorithms (e.g., Support Vector Machine, Multi-layer Perceptron) to show the effectiveness of our proposed Random Forest-based approach. Results obtained from this study provides implications and potential feasibility in providing a practically-applicable screening tool for identifying patient-intended malingering in hearing loss-related tests.