Parkinson’s Disease Prediction: Integrating Machine Learning Techniques and Drug Named Entity Recognition
Keywords:
Parkinson’s Disease, Machine Learning, Drug Named Entity Recognition, Support Vector Machine, Logistic Regression, XGBoost ClassifierAbstract
Early intervention and improved patient care depend on the ability to predict Parkinson's disease (PD). This research presents a detailed approach to PD prediction employing XGBoost (XGB), Support Vector Machine (SVM), and Logistic Regression. Data preprocessing ensures data quality, while visualization allows understanding of data patterns. Additionally, Drug Named Entity Recognition (DNER) extracts relevant medical information from web-scraped corpora. The goal of the study is to identify the development and progression of Parkinson's disease. The results of the study illustrate how well the XGB, SVM, and logistic regression models predict inaccurate information. Comparative analysis highlights each model's strengths and weaknesses. Furthermore, integrating DNER helps in giving better results after leveraging unstructured textual data. This research contributes to PD prediction by combining machine learning techniques with textual data mining. The approach enables early diagnosis and personalized treatment strategies, thereby enhancing patient outcomes.
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References
Kuo, Yang & Zheng, Yi & Kezhi, Lu & Chang, Kai & Wang, Ning & Shu, Zixin & Liu, Baoyan & Gao, Zhuye & Zhou, Xuezhong. (2020). PDGNet: Predicting Disease Genes Using a Deep Neural Network with Multi-view Features. IEEE/ACM Transactions on Computational Biology and Bioinformatics. PP. 1-1. 10.1109/TCBB.2020.3002771.
Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng. 2009 Apr;56(4):1015. doi: 10.1109/TBME.2008.2005954. PMID: 21399744; PMCID: PMC3051371.
Altham C, Zhang H, Pereira E. Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review. PLoS One. 2024 May 16;19(5):e0303644. doi: 10.1371/journal.pone.0303644. PMID: 38753740; PMCID: PMC11098383.
S.Sharanyaa, M.A.Gunavathie, Abitha.P, Sangeetha.K. Classification Of Parkinson’s Disease Using Logistic Regression. International Journal of Pure and Applied Mathematics
Doneti Sowmya, Dodla Kavya, J. Rashmitha, V. Satheesh kumar, Preethi Jeevan. Parkinson’s Disease Detection By MachineLearning Using SVM. International Research Journal of Engineering and Technology.
G Abdurrahman, M Sintawati. Implementation of xgboost for classification of parkinson’s disease. ICCGANT 2019. doi:10.1088/1742-6596/1538/1/012024
Kouli A, Torsney KM, Kuan WL. Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis. In: Stoker TB, Greenland JC, editors. Parkinson’s Disease: Pathogenesis and Clinical Aspects [Internet]. Brisbane (AU): Codon Publications; 2018 Dec 21. Chapter 1. Available from: https://www.ncbi.nlm.nih.gov/books/NBK536722/ doi: 10.15586/codonpublications.parkinsonsdisease.2018.ch1
C. Salvatore, A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri et al., “Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy,” Journal of Neuroscience Methods, vol. 222, pp. 230–237, 2014.
Saeed, Faisal & Al-Sarem, Mohammed & Al-Mohaimeed, Muhannad & Emara, Abdelhamid & Boulila, Wadii & Alasli, Mohammed & Ghabban, Drfahad. (2022). Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods. Computers, Materials & Continua. 71. 5639-5658. 10.32604/cmc.2022.023124.
Alshammri R, Alharbi G, Alharbi E and Almubark I (2023) Machine learning approaches to identify Parkinson's disease using voice signal features. Front. Artif. Intell. 6:1084001. doi: 10.3389/frai.2023.1084001
