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Khantsi, Thapelo Emmanuel (2024) Predictive Analytics & Modelling in Parkinson’s Disease for Severity Detection. Masters thesis, Dublin, National College of Ireland.
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Abstract
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by a range of motor and non-motor symptoms, significantly affecting patients' quality of life. This research employs predictive analytics and deep learning, with a focus on Long Short-Term Memory (LSTM) networks, to assess the severity of PD using a comprehensive multimodal dataset. The study combines motor and non-motor symptom data, along with genetic data from the Parkinson's Progression Markers Initiative (PPMI), for comprehensive assessment of PD severity detection. The proposed study captures the complex temporal patterns inherent in longitudinal medical data, with LSTM model achieving 91% accuracy in predicting disease severity. The study contributes to the healthcare domain by advancing the understanding of PD through a data-driven approach, highlighting how multimodal approach of integrating diverse modalities for precise severity detection can provide a holistic assessment of PD. The study also underscores the predictive capabilities of LSTM from PPMI data with R2 of 0.88 and RMSE of 0.33.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Siddig, Abubakr UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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: | 20 Aug 2025 09:35 |
| Last Modified: | 20 Aug 2025 09:35 |
| URI: | https://norma.ncirl.ie/id/eprint/8581 |
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