Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of Dataset
2.2. Image Processing and Feature Extraction
2.3. Machine Learning Algorithms
2.4. Model Training and Evaluation
3. Results
3.1. Algorithm Performance for Brain Age Prediction
3.2. Algorithm Performance for Gender Prediction
3.3. Comparative Study for Brain Age Prediction
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Age Range | No. of Subjects | ||
---|---|---|---|
Male | Female | Total | |
14–19 | 96 | 27 | 123 |
20–29 | 159 | 120 | 279 |
30–39 | 86 | 54 | 140 |
40–49 | 59 | 60 | 119 |
50–59 | 69 | 83 | 152 |
60–69 | 95 | 134 | 229 |
70–79 | 36 | 66 | 102 |
80–89 | 7 | 6 | 13 |
Total | 607 | 550 | 1157 |
No. | Feature | No. | Feature |
---|---|---|---|
1 | Left white matter | 18 | Right white matter |
2 | Left lateral ventricle | 19 | Right lateral ventricle |
3 | Left inferior lateral ventricle | 20 | Right inferior lateral ventricle |
4 | Left cerebellum white matter | 21 | Right cerebellum white matter |
5 | Left cerebellum cortex | 22 | Right cerebellum cortex |
6 | Left thalamus proper | 23 | Right thalamus proper |
7 | Left caudate | 24 | Right caudate |
8 | Left putamen | 25 | Right putamen |
9 | Left pallidum | 26 | Right pallidum |
10 | Left hippocampus | 27 | Right hippocampus |
11 | Left amygdala | 28 | Right amygdala |
12 | Left accumbens area | 29 | Right accumbens area |
13 | Left ventralDC | 30 | Right ventralDC |
14 | Left choroid plexus | 31 | Right choroid plexus |
15 | Left cerebral cortex | 32 | Right cerebral cortex |
16 | Cerebrospinal fluid | 33 | Brain stem |
17 | Third ventricle | 34 | Fourth ventricle |
Name | Purpose | Matrix | Symbol |
---|---|---|---|
Parameterized X Rotation | around the x-axis | ||
Parameterized Y Rotation | around the y-axis | ||
Parameterized Z Rotation | around the z-axis | ||
Controlled NOT (CNOT) | Entangle two qubits in a quantum circuit |
Regressors | Train (N = 925) | Test (N = 231) | ||||||
---|---|---|---|---|---|---|---|---|
MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | R2 | |
LR | 6.978 | 77.987 | 8.831 | 0.791 | 7.506 | 85.695 | 9.257 | 0.784 |
BR | 6.982 | 78.013 | 8.833 | 0.791 | 7.512 | 85.733 | 9.259 | 0.783 |
XGBoost | 4.437 | 33.259 | 5.767 | 0.911 | 7.639 | 104.394 | 10.217 | 0.736 |
RF | 2.809 | 14.118 | 3.757 | 0.962 | 8.275 | 118.161 | 10.870 | 0.701 |
SVR | 5.395 | 42.177 | 6.494 | 0.887 | 7.324 | 88.986 | 9.433 | 0.775 |
MLP | 6.118 | 62.193 | 7.886 | 0.834 | 7.103 | 83.184 | 9.121 | 0.790 |
VQC | 6.200 | 66.674 | 8.165 | 0.822 | 6.744 | 80.092 | 8.949 | 0.798 |
Classifiers | Train (N = 925) | Test (N = 231) | ||||||
---|---|---|---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | Accuracy | Precision | Recall | F1-Score | |
LR | 0.811 | 0.819 | 0.820 | 0.819 | 0.810 | 0.828 | 0.815 | 0.821 |
KNN | 0.838 | 0.837 | 0.851 | 0.844 | 0.779 | 0.779 | 0.798 | 0.788 |
XGBoost | 0.997 | 0.998 | 0.996 | 0.997 | 0.762 | 0.754 | 0.786 | 0.770 |
RF | 0.997 | 0.994 | 1.000 | 0.758 | 0.770 | 0.770 | 0.770 | 0.770 |
SVC | 0.835 | 0.845 | 0.840 | 0.843 | 0.771 | 0.787 | 0.780 | 0.784 |
MLP | 0.789 | 0.845 | 0.774 | 0.808 | 0.753 | 0.811 | 0.744 | 0.776 |
VQC | 0.809 | 0.864 | 0.791 | 0.826 | 0.818 | 0.885 | 0.794 | 0.837 |
Author | Method | Model Performance (MAE) | Prediction Performance (MAE) |
---|---|---|---|
Han, J. et al. [14] | ARD | 7.4790 | 8.0453 |
Proposed | VQC | 6.265 | 7.201 |
Author | Method | MAE | MSE | RMSE | R2 |
---|---|---|---|---|---|
Simfukwe, C. et al. [15] | BR | 3.310 | 18.280 | 4.280 | 0.300 |
Proposed | VQC | 3.302 | 16.675 | 4.083 | 0.425 |
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Jeon, Y.-J.; Park, S.-E.; Baek, H.-M. Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits. Brain Sci. 2024, 14, 401. https://doi.org/10.3390/brainsci14040401
Jeon Y-J, Park S-E, Baek H-M. Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits. Brain Sciences. 2024; 14(4):401. https://doi.org/10.3390/brainsci14040401
Chicago/Turabian StyleJeon, Yeong-Jae, Shin-Eui Park, and Hyeon-Man Baek. 2024. "Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits" Brain Sciences 14, no. 4: 401. https://doi.org/10.3390/brainsci14040401
APA StyleJeon, Y. -J., Park, S. -E., & Baek, H. -M. (2024). Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits. Brain Sciences, 14(4), 401. https://doi.org/10.3390/brainsci14040401