Advancements in Machine Learning and Deep Learning for the Detection of Neurological Disease: Parkinson’s Disease Case Study

Abstract

Parkinson disease (PD) is a neurological disease that worsens with time and is distinguished by the degeneration of dopamine-producing neurons in the brain, especially in the Substantia Nigra. This neuronal loss lead to the spectrum of motor and non-motor symptom’s, like postural instability, muscle rigidity, bradykinesia, tremors, autonomic dysfunction, sleep disturbances, mood disorders, cognitive changes, sensory symptoms, and others. The disease progression through stages from mild to severe significantly impacts patients' quality of life, affecting daily activities and functional independence. For immediate treatment and management of PD, early and precise detection is essential. Traditional detection methods have limitations, prompting the exploration of ML and DL to increase detection accuracy. ML methods like Neural network, Random Forest, Support Vector Machines (SVM), are employed to analyze diverse datasets including clinical assessments, genetic profiles, imaging results (MRI, PET), and voice recordings.

Recent studies have demonstrated promising results in PD detection using ML and DL models, leveraging features like gait patterns, vocal characteristics, and neuroimaging biomarkers. Challenges include data quality, feature selection, model complexity, interpretability, and clinical validation. Ethical considerations and integration into clinical workflows are also pivotal for widespread adoption. Future research directions aim to refine ML/DL models for early PD detection, validate their performance in clinical settings, and explore transfer learning approaches for optimizing model generalizability. Addressing these challenges will facilitate the development of robust diagnostic tools that improve patient outcomes and quality of life in PD management.