Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions
Abstract
:1. Introduction
2. Material and Methods
2.1. Participants and sMRI Acquisition
2.2. Imaging Processing
2.3. Feature Selection
2.3.1. Sequential Selection and Its Variants
2.3.2. T-Test
2.3.3. F-Score
2.3.4. Random Forest
2.4. SVM and Evaluating Metrics
2.4.1. Linear SVM
2.4.2. Competing Algorithms
2.4.3. Evaluating Metrics
2.5. Post Hoc Analysis
3. Results
3.1. Demographic Information
3.2. Performance of the Classifier
3.3. Post Hoc Analysis Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Index | Patients (n = 57) | Controls (n = 69) | T/χ2 | p |
---|---|---|---|---|
Age | 37.85 ± 13.63 | 35.32 ± 11.17 | 1.15 1 | 0.252 |
Gender (F/M) | 9/48 | 20/49 | 3.068 2 | 0.08 |
Method | Accuracy | Sensitivity | Specificity | AUC | Feature number | p |
---|---|---|---|---|---|---|
Full feature set | 53.97% | 56.14% | 55.07% | 0.5362 | 46 | 0.226 |
F score | 55.56% | 70.17% | 47.83% | 0.5751 | 11 | 0.062 |
T-test | 56.35% | 78.95% | 37.68% | 0.5238 | 11 | 0.329 |
Gini Index | 57.14% | 64.91% | 52.17% | 0.5700 | 13 | 0.088 |
SFS | 74.60% | 75.44% | 75.36% | 0.7442 | 5 | <0.001 |
SFFS | 71.43% | 80.70% | 66.67% | 0.7732 | 6 | <0.001 |
SBFS | 58.73% | 82.46% | 46.38% | 0.6293 | 30 | 0.007 |
SBE | 81.75% | 84.21% | 81.15% | 0.8241 | 17 | <0.001 |
Classifier | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|
SVM | 81.75% | 84.21% | 81.15% | 0.8241 |
KNN | 61.90% | 52.63% | 66.67% | 0.5960 |
RF | 58.73% | 47.37% | 72.46% | 0.5489 |
FNN | 57.14% | 63.16% | 59.42% | 0.5934 |
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Guo, Y.; Qiu, J.; Lu, W. Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions. Brain Sci. 2020, 10, 562. https://doi.org/10.3390/brainsci10080562
Guo Y, Qiu J, Lu W. Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions. Brain Sciences. 2020; 10(8):562. https://doi.org/10.3390/brainsci10080562
Chicago/Turabian StyleGuo, Yingying, Jianfeng Qiu, and Weizhao Lu. 2020. "Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions" Brain Sciences 10, no. 8: 562. https://doi.org/10.3390/brainsci10080562
APA StyleGuo, Y., Qiu, J., & Lu, W. (2020). Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions. Brain Sciences, 10(8), 562. https://doi.org/10.3390/brainsci10080562