Table 7.

Referenced literature that considered machine-learning-based Parkinson’s disease diagnosis.

Study	Contributions	Algorithm	Dataset	Data Type	Performance Evaluation
[84]	Parkinson’s disease	KMC and DT	Privately owned	Speech	Accuracy—95.56%
[16]	Parkinson’s disease subtype classification	DT, LR	PPMI	Tabular	Accuracy—98.3%, Sensitivity—98.41%, and Specificity—99.07%
[85]	Parkinson’s disease identification	KNN and ANN	Parkinson’s UI machine learning dataset	Tabular	ANN (Accuracy—96.7%)
[86]	Diagnosis system for Parkinson’s disease	ANN, KMC	Parkinsons dataset	Speech and sound	Accuracy—99.52%
[87]	identify Parkinson’s disease	SVM	NIHS	Speech and sound	Accuracy—83.33%, True positive—75%, False positive—16.67%