PloS one · 2025
Using machine learning for detection of Parkinson's disease and mild cognitive impairment.
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Dennis Anthaea-Grace Patricia, Martin Sarah L, Chen Robert, Gerretsen Philip, Strafella Antonio P
Аннотация
BACKGROUND: Parkinson's disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson's disease can cause significant disability, therefore, identification of Parkinson's disease and mild cognitive impairment in Parkinson's disease is imperative. Neuroimaging and biofluid biomarkers have been studied separately, however, research suggests that combining biomarkers may improve detection.
OBJECTIVES: We aimed to investigate using machine learning whether a combination of neuroimaging and biofluid biomarkers would result in more effective identification of Parkinson's disease and mild cognitive impairment.
METHODS: Utilizing the Parkinson's Progression Markers Initiative dataset, we applied two different machine learning approaches, support vector machine and random forest, to explore combinations of neuroimaging and cerebrospinal fluid biomarkers for detection.
RESULTS: Overall, both machine learning techniques had an equivalent performance. In general, in those models for detecting Parkinson's disease, DaT-SPECT performed better than biofluid biomarkers. In models for detecting Parkinson's disease patients with mild cognitive impairment, combining DaT-SPECT with phosphorylated-tau-181 resulted in higher accuracy, outperforming DaT-SPECT alone.
CONCLUSIONS: Classification for Parkinson's disease and mild cognitive impairment may be improved by combining neuroimaging with biofluid biomarkers through machine learning models.
OBJECTIVES: We aimed to investigate using machine learning whether a combination of neuroimaging and biofluid biomarkers would result in more effective identification of Parkinson's disease and mild cognitive impairment.
METHODS: Utilizing the Parkinson's Progression Markers Initiative dataset, we applied two different machine learning approaches, support vector machine and random forest, to explore combinations of neuroimaging and cerebrospinal fluid biomarkers for detection.
RESULTS: Overall, both machine learning techniques had an equivalent performance. In general, in those models for detecting Parkinson's disease, DaT-SPECT performed better than biofluid biomarkers. In models for detecting Parkinson's disease patients with mild cognitive impairment, combining DaT-SPECT with phosphorylated-tau-181 resulted in higher accuracy, outperforming DaT-SPECT alone.
CONCLUSIONS: Classification for Parkinson's disease and mild cognitive impairment may be improved by combining neuroimaging with biofluid biomarkers through machine learning models.