PARKISON’S DISEASE PREDICTION USING MACHINE LEARNING

Electronics and Communication Engineering

Parkinson’s Disease (PD) is a neurodegenerative disorder that primarily affects motor control, leading to symptoms such as tremors, rigidity, and bradykinesia. Early detection is critical to managing the progression of the disease, as current treatments focus on alleviating symptoms rather than curing the disease. Machine learning (ML) techniques have gained prominence in medical diagnostics, offering potential for early detection and improved accuracy in predicting Parkinson’s Disease. This paper presents an overview of various ML approaches for PD prediction, leveraging clinical and physiological data .The study explores different machine learning algorithms, including decision trees, support vector machines (SVM), k-nearest neighbours (KNN), and deep learning techniques, to predict the likelihood of Parkinson's Disease in individuals. The dataset typically used in these studies consists of clinical features such as voice recordings, gait analysis, and other non-invasive diagnostic information, as well as demographic data. Feature extraction, preprocessing, and dimensionality reduction techniques like Principal Component Analysis (PCA) are utilized to enhance model performance and accuracy. Performance evaluation of the models is based on metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). The results demonstrate that machine learning models, particularly ensemble learning techniques and deep learning models, can achieve high accuracy in classifying PD patients from healthy individuals. The application of these models in clinical settings could revolutionize the early diagnosis process, reducing the reliance on subjective clinical evaluations. This paper also addresses challenges in the field, such as the imbalance of data, interpretability of models, and the need for large, diverse datasets for robust model training. In conclusion, machine learning offers significant potential for enhancing the prediction and diagnosis of Parkinson’s Disease, improving patient outcomes through timely intervention and personalized treatment strategies. Further research and optimization are needed to fully integrate these models into clinical practice.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.