Machine Learning Methodology for Hearing Aid Prescription

This document is a technical report that outlines a proposed solution for prescribing hearing aids using machine learning techniques. It describes the integration of data-logging technology in modern hearing instruments, which records data separate from the audio stream, and how this data can be utilized to assist clinicians in making informed decisions. The report details the process of Knowledge Discovery in Databases (KDD) and its application in extracting meaningful patterns from audiology data. It explains various data mining techniques, including preprocessing, transformation, clustering, subgroup discovery, classification, and regression, emphasizing their roles in identifying suitable hearing aids based on patient data. Additionally, it discusses the evaluation of models using N-fold cross-validation to ensure predictive accuracy. The report concludes by highlighting the potential benefits of these models in detecting abnormal device behavior and assessing device performance over time, ultimately aiding clinicians in providing timely and cost-effective solutions for patients.