Publication Type : Conference Paper
Publisher : IEEE
Source : 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)
Url : https://doi.org/10.1109/ic2pct60090.2024.10486359
Campus : Bengaluru
School : School of Computing
Year : 2024
Abstract : Evaluating the extent of motor impairment is an important aspect in Parkinson’s disease, where the Unified Parkinson’s Disease Rating Scale (UPDRS) plays a crucial role. The UPDRS assessment is traditional, time-consuming and labor intensive. This paper presents a stacked regression model using Light gradient-boosting machine (LightGBM) and CatBoost to automate and enhance UPDRS score predictions. It is an important advancement in managing Parkinson’s disease that provides more efficient and individualized care for patients through ensemble learning.
Cite this Research Publication : S Anirudh, Manushri Tummala, Manushri Tummala, Sri Sai Suhas Sanisetty, K Dinesh Kumar, Nidal Nasser, A Hybrid Model for Accurate Prediction of Progression in Parkinson’s Disease, 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), IEEE, 2024, https://doi.org/10.1109/ic2pct60090.2024.10486359