A Generative AI Framework for Data Augmentation Employing Generative Adversarial Networks to Predict Parkinson’s Disease

Pervez, Irfan (2024) A Generative AI Framework for Data Augmentation Employing Generative Adversarial Networks to Predict Parkinson’s Disease. Masters thesis, Dublin, National College of Ireland.

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Abstract

Early detection of Parkinson’s disease (PD), a degenerative neurological ailment, can lead to more effective treatment. Traditional diagnostic testing methods often rely on clinical observations analysis, which might delay diagnosis. Voice sample analysis has recently been found to be a possible early sign of Parkinson’s disease (PD) because vocal problems are linked to motor weakness. Deep learning (DL) algorithms and other advanced AI based prediction models are not as useful as they could be because there are not enough big datasets that have been labeled. This study looks into whether synthetic data generated with a Generative Adversarial Network (GAN) can increase the accuracy of Parkinson’s disease prediction. We conducted experiments and generated multiple different versions based on the number of voice recordings of PD patients and evaluated the quality of synthetic data by predicting the PD using several machine and deep learning classifiers, including random forest, XGBoost, artificial neural networks (ANN), convolutional neural networks (CNN), fully connected neural networks (FCNN). The results show that using GAN-generated synthetic data improved diagnostic performance across many models, specifically deep learning models, where ANN and FCNN achieved the 99% of accuracy rate in predicting PD for all the synthetic data versions compared to 89% for original data, representing a significant 10% increase in this study. For machine learning models the precision, recall, and f1-score values were all around 98% in all the versions of synthetic data. These results also underline the need of generative artificial intelligence in improving medical diagnosis and point to possible uses in healthcare industry.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Niculescu, Hamilton UNSPECIFIED
Uncontrolled Keywords:	Parkinson’s Disease (PD); Generative Adversarial Networks (GAN); Synthetic Data Generation; Machine Learning (ML); Deep Learning (DL) Algorithms; precision; recall; f1-Score
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry > Neurology. Diseases of the Nervous System.
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 10:03
Last Modified:	04 Sep 2025 10:03
URI:	https://norma.ncirl.ie/id/eprint/8772

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