Name
Abstract | ||
|---|---|---|
In this paper, a new algorithm named cost sensitive and random forest alpha-weighted algorithm (alpha CSRF) is proposed as a diagnostic model for Alzheimer's disease (AD). In the new algorithm, cost-sensitive learning is introduced into random forest algorithm, and weighted sum terms of information gain ratio and misclassification cost decline ratio are constructed. In this model, [18F] AV1451 Tau-PET imaging data of areas of interest in the brain were selected as biological markers to classify disease course into three categories: normal control (NC), mild cognitive impairment (MCI) and AD. Experiment proved that this model is a dynamic model that can calculate the misclassification cost and the classification accuracy by adjusting the parameters. According to the actual requirements, the weighting parameters can be selected to obtain a model with better comprehensive performance. In this experiment, when the parameter is 0.6, the total error cost of the misclassification is quantified to 46.9, and the accuracy is 81.6%, which is the optimal comprehensive performance. Compared with other methods, the algorithm (alpha CSRF) proposed in this paper is more flexible, more practical and more robust. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1166/jmihi.2020.2921 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS |
Keywords | DocType | Volume |
Alzheimer's Disease,Random Forest,Cost-Sensitive Learning,[18F] AV1451 Tau-PET,Information Gain Ratio,Misclassification Cost Decline Ratio,Total Error Cost | Journal | 10 |
Issue | ISSN | Citations |
3 | 2156-7018 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haijing Sun | 1 | 4 | 2.41 |
| Anna Wang | 2 | 1 | 1.36 |
| Qing Ai | 3 | 8 | 3.85 |
| Yang Wang | 4 | 4 | 2.75 |