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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.08539 |
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| _version_ | 1866914677705932800 |
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| author | Li, Mingyang Liu, Hongyu Li, Yixuan Wang, Zejun Yuan, Yuan Dai, Honglin |
| author_facet | Li, Mingyang Liu, Hongyu Li, Yixuan Wang, Zejun Yuan, Yuan Dai, Honglin |
| contents | This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. Through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning Li, Mingyang Liu, Hongyu Li, Yixuan Wang, Zejun Yuan, Yuan Dai, Honglin Machine Learning Applications This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. Through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance. |
| title | Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2402.08539 |