Enhancing Parkinson's Disease Detection using AI Techniques

Abstract: One of the severe illnesses that causes uncontrollable and unexpected outcomes is Parkinson's disease(PD). People over 50 years of age are typically the ones who contract this illness. The patients' symptoms progressively become worse leading to a variety of abnormalities such as body part rigidity and abnormalities in speech and gait. In addition, the patients have sadness, sleep deprivation, memory problems, mental illness, and numerous other health problems. Parkinson's disease is caused by damage or death of neurons in the brain's basal ganglia, but scientists and doctors are unable to pinpoint the causes of this damage or death. Therefore, timely disease diagnosis and treatment can help patients avoid unanticipated life implications. The biggest benefit of this era is the application of Artificial Intelligence(AI) and machine learning(ML) in the healthcare industry, which facilitates and expedites diagnosis and prediction. In this paper, we have proposed a solution for Parkinson’s disease prediction. We have done a comparative analysis in terms of performance by implementing various Machine Learning algorithms that can be used for Parkinson’s disease predictions. Random Forest performs better than a lot of other ML methods showing 99% accuracy.