A Comparison of Feature Engineering Techniques for Hearing Loss

Miguel Rabuge, Nuno Lourenço

Abstract: Hearing Loss (HL) is becoming a concerning problem in modern society. The World Health Organization (WHO) stated in April 2021 that 5% of the world's population suffers from HL. As more medical data is being gathered, practitioners are trying to help solve medical field-related problems in an automated manner. Therefore, a method to preemptively predict HL is of utter importance. Given the current trend where information of varied types is being collected from multiple sources, a need to select and combine these pieces of information arises when aiming to maximize the prediction of HL.Feature Engineering (FE) is a time-consuming and error-prone task as it is usually made by human experts. In this paper, we aim to automatically boost Machine Learning (ML) models' performance by enhancing original features through evolutionary feature selection and construction. In concrete, we propose the FEDORA, an evolutionary Automated Machine Learning (AutoML) framework for FE. The proposed framework will be compared and analysed against state-of-the-art FE techniques.