Parkinson Prediction Using Voice Data and Machine Learning Algorithms

Parkinson’s disease is a prevalent neurological disorder affecting a significant portion of the global population. Early diagnosis is crucial for effective intervention and management, leading to improved patient outcomes. This study evaluates Parkinson’s disease by analyzing speech data, which typically shows distinct variations in affected individuals. Apply leverage advanced machine learning techniques and artificial intelligence to develop predictive models that can efficiently differentiate between speech data from Parkinson’s patients and healthy individuals. The model is trained on an extensive dataset of audio recordings, capturing all necessary attributes for accurate diagnosis. This work employs three machine learning algorithms K-Nearest Neighbor, Random Forest Classifier, and Decision Tree Classifier to identify the most effective model. The final approach demonstrates high efficacy in detecting Parkinson’s disease, enabling early diagnosis and personalized management strategies. This study underscores the transformative potential of machine learning and AI in Parkinson’s disease detection, presenting innovative approaches to patient care and management.