Parkinsons Disease Progression Prediction using Advanced Machine Learning Techniques

Title

Parkinsons Disease Progression Prediction using Advanced Machine Learning Techniques

Creator

Bhattacharya T.; Thomas K.T.; Mathew L.

Description

Parkinson's disease (PD) is a neurodegenerative condition that affects people over time and significantly lowers their quality of life. Patients with PD experience both motor and non-motor symptoms. Through clinical evaluation, the Unified Parkinson's Disease Rating Scale (UPDRS) is used to quantify the severity of Parkinson's disease. No definitive diagnostic tests for PD currently exist. Emerging machine learning techniques show potential to forecast future UPDRS scores for making informed medical decisions and enable better disease management. This paper studies research leveraging proteomic data to forecast PD prognosis, focusing on advanced machine learning techniques like CatBoost Regressor, ElasticNet, XGBoost Regressor, RandomForest Regressor, ExtraTrees Regressor and DecisionTree Regressor. 2024 IEEE.

Source

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

CatBoost regressor; extraTrees regressor; machine learning; parkinsons' disease; proteomic data; unified parkinson's disease rating scale (UPDRS)

Coverage

Bhattacharya T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Thomas K.T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Mathew L., Christ (Deemed To Be) University, Department of Data Science, Pune, India

Rights

Restricted Access

Relation

ISBN: 979-835037809-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICEECT61758.2024.10739044

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211187364&doi=10.1109%2fICEECT61758.2024.10739044&partnerID=40&md5=770985e5c402d6feaf3e55beb81e911b