  In this project, we have explored machine learning approaches for predicting
hearing loss thresholds on the brain's gray matter 3D images. We have solved
the problem statement in two phases. In the first phase, we used a 3D CNN model
to reduce high-dimensional input into latent space and decode it into an
original image to represent the input in rich feature space. In the second
phase, we utilized this model to reduce input into rich features and used these
features to train standard machine learning models for predicting hearing
thresholds. We have experimented with autoencoders and variational autoencoders
in the first phase for dimensionality reduction and explored random forest,
XGBoost and multi-layer perceptron for regressing the thresholds. We split the
given data set into training and testing sets and achieved an 8.80 range and
22.57 range for PT500 and PT4000 on the test set, respectively. We got the
lowest RMSE using multi-layer perceptron among the other models.
  Our approach leverages the unique capabilities of VAEs to capture complex,
non-linear relationships within high-dimensional neuroimaging data. We
rigorously evaluated the models using various metrics, focusing on the root
mean squared error (RMSE). The results highlight the efficacy of the
multi-layer neural network model, which outperformed other techniques in terms
of accuracy. This project advances the application of data mining in medical
diagnostics and enhances our understanding of age-related hearing loss through
innovative machine-learning frameworks.
