Metaheuristic-driven machine learning study for early detection and classification of Parkinson's disease using feature prioritization with pelican optimization algorithm.

Parkinson disease (PD) is a degenerative disorder of the brain and afflicts approximately 6 in 10 people aged 50 years or older. PD patients have motor and speech problems, so regular visits to and monitoring of the patients are hard. It is necessary to detect the presence of PD promptly and accurately, since early treatment will contribute greatly to enhancing patients' lives. As the number of aging people increases, there is a great demand for noninvasive, reliable, and remote diagnosis. In the current work, we studied 31 patients with PD and healthy subjects, their voice recordings, to create an automatic classification system. A Light Gradient Boosting Machine (LightGBM) classifier was adapted and boosted using metaheuristic-based feature selection (FS), namely the Pelican Optimization Algorithm (PAO). Hyperparameter optimization was made to optimize predictive performance. The models have been assessed on typical classification measures, i.e., accuracy, sensitivity, specificity, precision, and AUC. We classified using the baseline LightGBM classifier, with an accuracy of 95%. The resulting model had a better prediction accuracy of 97% after using PAO-based FS and hyperparameter optimization. More than that, the model was also sensitive, specific, precise, and had a high area under the curve, which validates its effectiveness at classifying PD. The paper shows that FS and hyperparameter tuning are effective approaches when applied to voice data and combined with LightGBM to detect PD as early as possible. The results point to the promise of noninvasive diagnostic systems based on the use of telemedicine to allow early intervention and enhance the lives of people with PD.