Robust machine learning method for imputing missing values in audiograms collected in children.

ABSTRACT

OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA). DESIGN: Retrospective study. STUDY SAMPLE There were 206 audiograms included from 206 children with sensorineural hearing loss. Nested cross-validation was used for evaluating the performance of the CA and ML. Six audiogram prediction simulations were performed in which either one or two thresholds across 0.5-4 kHz from complete audiograms in the dataset were labelled. Missing thresholds at the remaining frequencies were then predicted using the CA and ML in each simulation. The accuracy of the ML algorithm was determined by comparing the median average absolute threshold differences between the CA and ML using Wilcoxon signed-rank test. The reliability between runs of the ML was also assessed with Cronbach's alphas. RESULTS: The median average absolute threshold differences in ML (5-8 dBHL) were statistically significantly lower than those in CA (6.25-10 dBHL) in all six simulations (p value < 0.05). The ML algorithm was also found to be reliable to predict the audiograms in all six simulations (α > 0.9). CONCLUSION: Using the ML to predict the children's audiograms was reliable and more accurate than using the CA.