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@article{167419,
        author = {HARSHA R and Indira B},
        title = {Enhancing Parkinson's Disease Progression Prediction through Integrated Proteomic and Clinical Data using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1699-1702},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167419},
        abstract = {Parkinson's disease (PD) is a progressive neurodegenerative disorder with substantial clinical implications. Accurate prediction of disease progression is critical for effective patient management and treatment planning. This study integrates proteomic data with clinical metrics to develop predictive models for PD progression utilizing advanced machine learning techniques. We applied Random Forest and Gradient Boosting algorithms, assessed their performance through accuracy and F1-score, and determined key biomarkers through feature importance analysis. Our findings demonstrate that combining proteomic and clinical data improves predictive accuracy and offers valuable insights into disease mechanisms.},
        keywords = {Parkinson's disease, proteomic data, machine learning, Random Forest, Gradient Boosting.},
        month = {September},
        }