Prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data

Cochlear disorder is an audio impairment issue, which causes difficulty in understanding human speech. These disorders can cause difficulty in speech recognition, communication, and language development. Intelligent approaches are proven to be efficient and novel approaches for performing various challenging tasks in the healthcare industry. The primary objective of this study is to use machine learning and computer vision domain, to create a web-based platform enabling early detection of the disorders. Computer vision with a classification model is used for achieving the objective. The model is trained on the static custom audiology dataset formulated from the UCI machine learning repository. Cross-validation over various classification algorithms like Logistic Regression, Decision Tree, Support Vector Classifier, K-Nearest Neighbors, and Multi-Layer Perceptron is performed and is proven that Multi-Layer Perceptron suits the dataset. Application for the purpose is developed using Python flask and is deployed for validation.