Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Abstract

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

“…Ghani et al [1] developed a decision-support system for diagnosing the symptoms of hearing loss using machine-learning techniques. They used the frequent pattern growth (FP-Growth) algorithm as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier.…”

Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage

“…This approach was originally implemented to analyze the shopping patterns of customers in malls to uncover the combinations of products that customers often buy together, thus helping malls optimize the arrangement of goods to increase revenue [29]. Currently, AC is widely used in various fields, such as biomedicine [30,31], traffic safety [32,33], and phishing website detection [34,35]. Typically, the classification by AC involves three main phases [36]: (1) adopting the methods in ARM to discover all association rules between attributes and classes; (2) filtering rules using relevant evaluation indicators to obtain strong association rules; and (3) using the obtained strong association rules as classifiers.…”