Early Diagnosis of Parkinson’s Disease Using Machine Learning

Parkinson's disease presents a frequent challenge in neurological practice due to its complex nature. Diagnosis and management are often intricate as patients exhibit a broad spectrum of motor and non-motor symptoms. Given the clinical nature of diagnosis, distinguishing Parkinson's disease from other neurological disorders can be particularly demanding due to the overlap of symptoms with various conditions. This proposed method aims to improve diagnostic accuracy and facilitate proactive intervention by combining clinical data with machine learning algorithms. This paper describes the important aspects of the Random Classifier algorithm-based prediction system using the Streamlit framework and then evaluates the system's performance. Utilizing an extensive dataset from several healthcare facilities, feature engineering methods extract pertinent features, which are subsequently supplied into the Random Forest Classifier for training the model. The model shows high predictive accuracy, sensitivity, specificity, and robust generalization capabilities.