Using machine learning for detection of Parkinson’s disease and mild cognitive impairment
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".