Machine Learning and Deep Learning Techniques for Early Parkinson’s Disease Diagnosis

Abstract

Parkinson’s disease (PD) is an increasingly developing neurological disorder that affects both motor and non-motor functions, resulting in a reduction of quality of life. Early detection of PD is highly challenging, as the occurrence of noticeable symptoms happens only after considerable neurodegeneration. Similarly, conventional diagnostic techniques rely more on subjective clinical judgment. To overcome these problems, non-invasive biomarkers based on speech, sensor, retinal, and neuroimage modalities are extensively investigated using computational techniques. A wide variety of analytical techniques, including conventional machine learning, ensemble, hybrid, and optimization-based approaches, are employed for the identification of significant patterns associated with PD. To this purpose, an extensive evaluation of existing algorithms and multimodal diagnostic techniques to assess their effectiveness, advantages, and limitations across different data modalities. Deep neural network models further improve the diagnostic process by automatically learning hierarchical and high-level representations directly from data. By using this ability, complex and nonlinear patterns of Parkinson’s disease progression can be effectively captured for accurate feature learning and early diagnosis.