Classification of Hearing Loss Degrees with Naive Bayes Algorithm

Classification of Hearing Loss Degrees with Naive Bayes Algorithm

classification; hearing loss degrees; naive bayes

According to the World Health Organization (WHO), hearing loss is one of the fourth highest causes of disability. The number
of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing the 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 dataset from the open-access repository Zenodo 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 to 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

Okky Putra Barus, Romindo, Jefri Junifer Pangaribuan

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Elektronik: 2580-0760

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