Classification of Hearing Loss Degrees with Naive Bayes Algorithm

Abstract

According to the World Health Organization (WHO), hearing loss is one of the fourth leading causes of disability. The number of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing hearing loss, leading to delays in providing treatment. To solve this problem, one solution to deal with this is early identification to detect the degree of hearing loss. This research will use machine learning to classify the degree of hearing loss. The algorithm implemented in this study is naive Bayes. This study uses a data set from the Zenodo open access repository with 3105 raw data and 19 features. This study evaluates the performance of overall accuracy, precision, recall, and f1-score and classified four classes: mild, moderate, moderately severe, and severe. The methodology classification stages in this study include data preprocessing, data training, data testing, and evaluation. From evaluating the performance of the Naive Bayes algorithm, the classification results obtained the highest impacts in the form of 94% overall accuracy, 100% precision, 100% recall and 97% f1-score in classifying the degree of hearing loss.

Keywords

• classification
• hearing loss degrees
• naive bayes