Summary of Machine Learning Classification Of Alzheimer’s Disease Stages Using Cerebrospinal Fluid Biomarkers Alone, by Vivek Kumar Tiwari et al.
Machine Learning Classification of Alzheimer’s Disease Stages Using Cerebrospinal Fluid Biomarkers Alone
by Vivek Kumar Tiwari, Premananda Indic, Shawana Tabassum
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models have been used to classify different stages of Alzheimer’s disease based on cerebrospinal fluid biomarker levels. Researchers analyzed electronic health records from the National Alzheimer’s Coordinating Centre database and found that machine learning classifiers such as Ensemble Boosted Tree, Logistic Regression, and Ensemble Bagged Tree can accurately diagnose early-stage Alzheimer’s disease with high accuracy (up to 84.4%). These findings have the potential to aid clinicians in making informed decisions about diagnosis, monitoring, and intervention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Alzheimer’s disease is a serious condition that affects millions of people worldwide. The key challenge is diagnosing it early enough to make a difference. Scientists have been searching for ways to do this using biomarkers found in cerebrospinal fluid (the liquid surrounding the brain). This study used machine learning models to analyze these biomarkers and predict which stage of Alzheimer’s someone has. The results show that certain models can accurately diagnose the disease at its early stages, giving doctors a powerful tool to help patients. |
Keywords
* Artificial intelligence * Logistic regression * Machine learning
