PARKINSON’S DISEASE PREDICTION USING MACHINE LEARNING

Abstract

Parkinson's disease (PD) is a condition that affects a substantial number of individuals worldwide, leading to impairments in motor functions and a decline in overall quality of life. The timely identification of PD is vital for effective intervention and better patient outcomes. This study investigates the Utilization of machine learning methods in predicting and diagnosing Parkinson's disease using clinical and biomedical data.The dataset is pre-processed to address missing values and standardize features. Subsequently, various ML algorithms, including Support Vector Machines (SVM) are employed to develop predictive models.The research evaluates the performance of each model through rigorous training and testing procedures, utilizing metrics such as accuracy, sensitivity, and specificity. Feature importance analysis is conducted to identify key factors contributing to the predictive accuracy of the models. Additionally, the study investigates the impact of different feature subsets on model performance.The proposed ML models exhibit promising results in accurately predicting Parkinson's disease based on diverse sets of features. The research contributes to the ongoing efforts to develop non-invasive and efficient diagnostic tools for Parkinson's disease, providing a foundation for further studies and potential integration into clinical practice. A linear kernel utilized in a support vector machine classifier trained on a dataset with diverse voice-related features, demonstrated exceptional performance in predicting Parkinson's disease. The user interface was designed with a focus on availability, make secure that users can easily navigate and understand the application. The prediction result, either An individual has been Received a diagnosis of Parkinson's disease or The person does not have Parkinson's disease," is displayed. The model achieved an accuracy of (87%) on the test setThe high training accuracy of (86%) further affirms the model's capability to capture underlying patterns in the training data. Keywords: Parkinson's disease, machine learning, predictive modelling, feature importance, neurodegenerative disorders.