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@article{193624,
        author = {Moulieswaran R and Revanth M J and Gokul S and Karthikeyan B},
        title = {Machine Learning Based Early Detection System for Parkinson’s Disease},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1043-1047},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193624},
        abstract = {Parkinson’s Disease (PD) is a neurodegenerative disorder that causes the deterioration of motor coordination, speech, and other cognitive functions. Early detection is vital to reduce the progression rate and enhance the quality of life for patients suffering from Parkinson’s Disease. The traditional methods of detecting Parkinson’s Disease are based on observation and expertise, which may lead to delayed detection in the early stages. This study proposes the development of a machine learning-based intelligent system to detect Parkinson’s Disease in its early stages using voice-derived biomedical features. The intelligent system will be based on the classification algorithm XGBoost, which is highly efficient and has the capability to produce high accuracy in the classification process using structured medical datasets. The biomedical features will be collected from the patient’s voice, which will be affected in the case of Parkinson’s Disease, and the XGBoost algorithm will be implemented to classify the patient’s voice as normal or affected with Parkinson’s Disease. The proposed intelligent system will be implemented using the Python Flask framework to create a web-based application that will be used to support the healthcare professionals in the classification process.},
        keywords = {Parkinson’s Disease, Machine Learning, XGBoost, Voice Analysis, Biomedical Signal Processing, Early Detection, Data Science, Predictive Modeling, Healthcare Informatics, Clinical Decision Support System.},
        month = {March},
        }