Early Detection of Parkinson’s Disease Using Machine Learning

Parkinson’s Disease (PD) is a chronic neurodegen- erative disorder that significantly impacts motor and non-motor functions, affecting millions of people globally. While motor symptoms such as tremors, rigidity, and bradykinesia are the most recognizable features, subtle non-motor signs, including vocal impairments, often emerge in the early stages of the disease. Early diagnosis of PD is crucial, as timely interventions can slow disease progression and improve patients’ quality of life. Traditional diagnostic methods, reliant on clinical observations and imaging, are often subjective, expensive, and inaccessible in resource-limited settings. This research introduces a real-time, non-invasive system for PD prediction using voice analysis. The proposed system leverages advanced machine learning techniques to identify vocal biomarkers indicative of Parkinson’s. Features such as Mel- frequency cepstral coefficients (MFCCs), jitter, shimmer, and harmonics-to-noise ratio (HNR) are extracted from recorded voice samples, providing a quantitative basis for early disease detection. By employing deep learning architectures like CNN- LSTM, the system achieves high accuracy in distinguishing Parkinsonian voices from healthy ones, even in challenging environments. Designed for real-time operation, the system integrates voice input, feature extraction, and predictive analysis into a seamless pipeline with minimal latency. The implementation emphasizes scalability, allowing the system to handle diverse linguistic and demographic variations. Evaluation on benchmark datasets, such as the Parkinson’s Telemonitoring dataset, demonstrates the sys- tem’s robustness, achieving significant improvements in accuracy and processing speed compared to existing methods. The system’s user-friendly interface ensures accessibility for clinical and home- based applications. This study highlights the transformative potential of voice- based diagnostic systems driven by machine learning