Early detection of Parkinson’s disease and analysis using speech signal

Parkinson's Disease (PD) is a progressive neurodegenerative condition that effects motor and non- motor functions, such as speech and movements. Early diagnosis of PD continues to be a clinical challenge because of its subtle early symptoms. In this paper, we present a machine learning-based approach to early diagnosis of Parkinson's Disease from voice recordings, improved with nature-inspired feature selection methods. Audio features from the MDVR-KCL dataset are preprocessed and evaluated using several classifiers, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Naïve Bayes. For enhanced classification accuracy and model simplicity, we use Firefly Algorithm (FFA) and Particle Swarm Optimization (PSO) for feature selection. From experimental findings, we have the best accuracy of 86.67% with the combination of XGBoost and PSO, proving the efficiency of bio-inspired optimization in improving PD detection. The suggested method offers a affordable and scalable solution for early Parkinson's Disease diagnosis, which can also be used in real-world telehealth and mobile-based screening systems.