Enhancing Parkinson’s Disease Diagnosis Using Machine Learning: A Comparative Study

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Abstract Parkinson's disease is the second most common neurological illness in the world, exactly after Alzheimer's disease. It is the long-term degenerative condition of a human being's central nervous system that primarily affects people over the age of sixty. Parkinson's disease is one of the many neurological conditions that progress over time. Problems with movement are the first symptoms. Initial indicators of the disease could also include vocal dysfunction. Humans diagnosed with Parkinson's have vocal abnormalities that impair their voice's loudness and cause difficulty in pronunciation. As a result, Parkinson's disease can be diagnosed using vocal measures. People may notice issues with common movements, tremors, stiffness in the limbs or trunk, or even decreased balance as neurons (nerve cells) in areas of the brain are weakened, get injured, or die. Patients may struggle with walking, talking, or accomplishing other simple tasks as these symptoms become more noticeable. However, like many other diseases and disorders, these symptoms also appear in other conditions. Thus, it is not necessary for everyone with one or more of these symptoms to have Parkinson's disease. This paper intends to implement 4 base machine learning classifiers and 4 proposed ensemble classifiers to compare and select the best possible model through a dataset of 23 attributes and around 177 records. It is concluded that the ensembles perform far better and are the best interchange between the XG Boost and Random Forest classifier.

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