Parkinson Disease (PD) is a neurodegenerative disorder, progressive in nature which has no cure. The delay of PD progression is possible by incorporation of early diagnosis system. Early diagnosis can be made effective and accurate by the usage of Artificial Intelligence (AI) techniques. AI is prevalent in almost all the fields due to its intuitiveness and accuracy which covers the small applications in education sectors to the large applications in healthcare diagnosis system. This paper aims to provide an intensive review in the advancements of PD diagnosis by providing taxonomy, classification of PD diagnosis system and mapping the symptoms with its modalities. This paper also focuses on presenting the advancements of PD Clinical Decision Support System (CDSS) along with telemonitoring and telediagnosis in chronological order. A generic framework is presented for early PD diagnosis with the state-of-the-art technique. The paper is concluded with challenges and future prospects in the field of early diagnosis of PD.
Parkinson Disorder (PD) is a neurological disorder which is progressive in nature and has no cure. Early diagnosis of PD plays a key role in delaying the progression of the disorder. Dysphonia is the most prominent early symptom which is exhibited by approximately 90% of PD patients. Voice features based early diagnosis with the integration of Artificial Intelligence plays a prominent role in providing accurate, non-invasive, and robust predictions to PD patients. This paper focuses on providing comparative and experimental analysis of Machine Learning (ML) algorithms for the prediction of PD based on the voice features dataset retrieved from the UCI repository. This paper presents the results from the four sampling experiments conducted with different traditional ML algorithms for the retrieved voice dataset. The results of this study make it evident that Naïve Bayes provides a highest accuracy of 89% when compared to other ML algorithms. This study helps in identifying the best ML algorithm among the traditional ML algorithms for PD prediction based on voice features dataset.
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