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@article{177629,
        author = {Ahaladitha Thamada and Shirisha Kampati},
        title = {Parkinson's Disease Detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1361-1367},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177629},
        abstract = {Parkinson’s Disease is a special type of neurological condition which has an impact on people across the globe. For efficient treatment of this disease, early detection is crucial. It is a neurodegenerative disorder that affects the motor functions of a human. Research has demonstrated that early signs of the disease include changes in speech such as those in speaking rate and voice quality. This model proposes a brand new approach for the early detection of Parkinson’s using the speech alterations. Previously, machine learning has been used to detect the presence of the disease by processing ‘spiral’ images drawn by the healthy and Parkinson’s patients. However, due to minimum accuracy voice and speech alterations have been considered as a reliable parameter for the identification of the disease. The model is trained using various machine learning algorithms from which 80% data is used for training and 20% is used for testing. The model has an average training accuracy of 96.47% and an average testing accuracy of 91.67% making it efficient and accurate when applied to the dataset of speech samples from people with and without the disease.},
        keywords = {Parkinson’s Disease, early detection, neurological condition, speech alterations, machine learning, voice quality, speech analysis.},
        month = {May},
        }