Classification of Alzheimer’s Disease using Machine Learning Techniques
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a commonly known and widespread neurodegenerative disease which causes cognitive impairment. Although in medicine and healthcare areas, it is one of the frequently studied diseases of the nervous system despite that it has no cure or any way to slow or stop its progression. However, there are different options (drug or non-drug options) that may help to treat symptoms of the AD at its different stages to improve the patient’s quality of life. As the AD progresses with time, the patients at its different stages need to be treated differently. For that purpose, the early detection and classification of the stages of the AD can be very helpful for the treatment of symptoms of the disease. On the other hand, the use of computing resources in healthcare departments is continuously increasing and it is becoming the norm to record the patient’ data electronically that was traditionally recorded on paper-based forms. This yield increased access to a large number of electronic health records (EHRs). Machine learning, and data mining techniques can be applied to these EHRs to enhance the quality and productivity of medicine and healthcare centers. In this paper, six different machine learning and data mining algorithms including k-nearest neighbors (k-NN), decision tree (DT), rule induction, Naive Bayes, generalized linear model (GLM) and deep learning algorithm are applied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset in order to classify the five different stages of the AD and to identify the most distinguishing attribute for each stage of the AD among ADNI dataset. The results of the study revealed that the GLM can efficiently classify the stages of the AD with an accuracy of 88.24% on the test dataset. The results also revealed these techniques can be successfully used in medicine and healthcare for the early detection and diagnosis of the disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| 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 | 0.001 |
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".