Summary of Decoding Cognitive Health Using Machine Learning: a Comprehensive Evaluation For Diagnosis Of Significant Memory Concern, by M. Sajid et al.
Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern
by M. Sajid, Rahul Sharma, Iman Beheshti, M. Tanveer
First submitted to arxiv on: 11 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a comprehensive evaluation of machine learning models for detecting significant memory concern (SMC), a crucial step in proactive cognitive health management, especially in an aging population. The study utilizes the Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) dataset and analyzes T1W magnetic resonance imaging (MRI) scans to identify SMC. The paper reviews state-of-the-art models within the randomized neural networks (RNNs) and hyperplane-based classifiers (HbCs) family, focusing on deep random vector functional link (dRVFL), ensemble dRVFL (edRVFL), Kernelized pinball general twin support vector machine (Pin-GTSVM-K), Linear Pin-GTSVM (Pin-GTSVM-L), and Linear intuitionistic fuzzy TSVM (IFTSVM-L). The study emphasizes the importance of feature selection and model choice in achieving effective classifiers for SMC diagnosis. The results are evaluated using various performance metrics, highlighting the suitability of this framework for automated and accurate assessment of SMC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help researchers identify significant memory concern (SMC) accurately. It uses special machines called neural networks and computer algorithms to analyze brain scans from older adults with SMC or without it. The study looks at different types of neural networks and how well they work in identifying SMC. The results show that some neural networks are better than others at identifying SMC, especially when using certain features from the brain scans. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Support vector machine
