Parkison’s Disease Prediction Using Machine Learning: A Review

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Parkison’s Disease Prediction Using Machine Learning: A Review

DOI : 10.46335/IJIES.2024.9.1.2

Authors : Krutik Dharange, Amod Polkamwar, Yash Tarale_, Om Kute, Akhil Bisen

Abstract – The majority of studies in this decade of rapid advancements in the medical sciences neglect to address age-related illnesses. These are illnesses that almost never fully recover and only show symptoms at a much later period. The second most frequently identified neurodegenerative brain illness is Parkinson's disease (PD). It may be argued that the patients suffer excruciating pain and that the condition is nearly incurable. All of them demonstrate how urgently effective, trustworthy, and broad diagnostic methods for Parkinson's disease are needed. For a problem this serious, the diagnosis must be auto       mated in order to produce accurate and trustworthy results. Speech measurements and indicators are crucial in predicting Parkinson's disease (PD) because the majority of PD patients exhibit some form of speech impairment or dysphonia. The goal of the paper is to compare different machine learning model to predict the Parkinson’s disease. This will enable the development of an accurate and efficient model that will aid in the early diagnosis of the condition, which will in turn enable medical professionals to aid in the treatment and recovery of PD patients. We intend to use the Parkinson's dataset, which was obtained from Kaggle, for the previously described reason.