Summary of Utilizing Machine Learning and 3d Neuroimaging to Predict Hearing Loss: a Comparative Analysis Of Dimensionality Reduction and Regression Techniques, by Trinath Sai Subhash Reddy Pittala et al.
Utilizing Machine Learning and 3D Neuroimaging to Predict Hearing Loss: A Comparative Analysis of Dimensionality Reduction and Regression Techniques
by Trinath Sai Subhash Reddy Pittala, Uma Maheswara R Meleti, Manasa Thatipamula
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates machine learning techniques for predicting hearing loss thresholds from brain’s gray matter 3D images. The approach is divided into two phases: dimensionality reduction and feature extraction using a 3D convolutional neural network (CNN) model, followed by training standard machine learning models to predict hearing thresholds. In the first phase, autoencoders and variational autoencoders are employed for dimensionality reduction. Then, random forest, XGBoost, and multi-layer perceptron models are trained on the reduced features to regress the hearing loss thresholds. The study achieves an 8.80 range and 22.57 range for PT500 and PT4000, respectively, using a split training-test dataset, with the lowest root mean square error (RMSE) obtained from the multi-layer perceptron model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores new ways to predict hearing loss by analyzing brain images. The team uses special computer models called neural networks to shrink and then expand the image data. They then train other machine learning models on this reduced data to make predictions about hearing loss. The results show that this approach is quite accurate, with some models performing better than others. Overall, this study contributes to our understanding of how to use brain images to diagnose hearing problems. |
Keywords
» Artificial intelligence » Cnn » Dimensionality reduction » Feature extraction » Machine learning » Neural network » Random forest » Xgboost
