In recent years, there have been remarkable advancements in machine learning. The rise in computational power and the abundance of data have made these systems indispensable in various fields, such as disease follow-up. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder worldwide. The most notable symptoms of PD are bradykinesia, rest tremor, postural instability, and rigidity. In clinical practice, a variety of diagnostic techniques, including the finger-tapping test, the walk test, the gait analysis, and the evaluation of speech impairment, are used to examine these symptoms. According to the Movement Disorder Society's published guidelines, the finger-tapping test (FTT) is one of the most frequently administered evaluations of bradykinesia, but manual visual evaluations can result in score discrepancy due to clinicians' subjectivity.