A Study to Evaluate Various Machine Learning Approaches for Early Prediction of Neurodegenerative Diseases
Bibliographic record
Abstract
Alzheimer's and Parkinson's diseases are prevalent neurodegenerative disorders among the elderly, often with a genetic predisposition, posing significant challenges in early diagnosis and intervention. Timely detection is crucial to slowing disease progression and improving patient outcomes. This study systematically evaluates the efficacy of various machine learning algorithms for early prediction of neurodegenerative diseases using two distinct datasets: handwriting samples for Alzheimer's and biomedical voice measurements for Parkinson's. Ten widely used machine learning models, including logistic regression, support vector machines, random forest, decision trees, K-nearest neighbor, autoencoder, hybrid model, Gaussian mixture model, naïve Bayes, and gradient boosting, were as-sessed for their predictive performance. The results indicate that the random forest model consistently outperformed others for both Alzheimer's and Parkinson's by achieving 91% and 94% accuracy respectively. These findings highlight the potential of machine learning techniques in enhancing early diagnostic tools for neurodegenerative diseases.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".