Summary of Intelligent Diagnosis Of Alzheimer’s Disease Based on Machine Learning, by Mingyang Li et al.
Intelligent Diagnosis of Alzheimer’s Disease Based on Machine Learning
by Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang, Yuan Yuan, Honglin Dai
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 researchers have developed a novel approach to detect Alzheimer’s disease in its early stages using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. By employing innovative data preprocessing strategies, including random forest algorithm-based missing data filling and outlier handling, they were able to fully utilize the limited data resources. The study identified features strongly correlated with AD diagnosis through Spearman correlation coefficient analysis and built three machine learning models: random forest, XGBoost, and support vector machine (SVM). Notably, the XGBoost model achieved an accuracy of 91%, outperforming the other two models. This research has significant implications for early disease detection and provides valuable insights into Alzheimer’s disease progression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect Alzheimer’s disease has been discovered using a special dataset called ADNI. Scientists used clever ways to fix missing data and make sure all the information was useful. They found some clues that are strongly linked to whether someone has Alzheimer’s or not. Then, they built three machines that can learn from these clues: random forest, XGBoost, and support vector machine (SVM). The best one was XGBoost, which got 91% correct! This helps us understand how Alzheimer’s starts and gets worse. |
Keywords
* Artificial intelligence * Machine learning * Random forest * Support vector machine * Xgboost
