A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder
Abstract
:1. Introduction
2. Related Research Work
2.1. Signal Processing Based Approaches
2.2. Functional Connectivity Based Approaches
2.3. Deep Learning-Based Approaches
3. Materials and Methods
3.1. Dataset
3.2. Preprocessing the Dataset
3.3. Methodology
3.3.1. Stage 1, Teacher Neural Network
3.3.2. Stage 2, Student Neural Network
3.3.3. Stage 3, Feature Extraction Module
3.3.4. Algorithm
Algorithm 1 Ranking Discriminating Features Algorithm |
Input:
Output:
|
Algorithm 2 Sequential Forward Feature Selection Algorithm |
Input: , , CLF Output:
|
4. Experimentation and Results
Experimental Settings
5. Feature Selection
5.1. Justification of Selected Features
5.1.1. UnderFitting
5.1.2. Over Fitting
5.1.3. Selected Features
5.2. Combined Dataset Accuracy Using 10-Fold Cross Validation
5.3. Site Wise Accuracy Using 5-Fold Cross Validation
6. Discussion
6.1. Connectogram for the Brain Region Network
6.1.1. Connectivity in the Intralobe Network
6.1.2. Connectivity in the Interlobe Network
6.2. Alterations in Brain’s Hemisphere Connectivity Patterns
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ABIDE | Autism Brain Imaging Data Exchange |
ASD | Autism Spectrum Disorder |
DT | Decision Trees |
LD | Linear Discriminant |
rs-fMRI | Resting State Functional Magnetic Resonance Imaging |
RF | Random Forests |
SVM | Support Vector Machine |
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Participants | |||
---|---|---|---|
Sr No. | Site Name | Autistic Subjects | Healthy Controls |
1 | Caltech | 19 | 18 |
2 | CMU | 14 | 13 |
3 | KKI | 20 | 28 |
4 | Leuven | 29 | 34 |
5 | MaxMun | 24 | 28 |
6 | NYU | 75 | 100 |
7 | OHSU | 12 | 14 |
8 | OLIN | 19 | 15 |
9 | PITT | 29 | 27 |
10 | SBL | 15 | 15 |
11 | SDSU | 14 | 22 |
12 | Stanford | 19 | 20 |
13 | Trinity | 22 | 25 |
14 | UCLA | 54 | 44 |
15 | UM | 66 | 74 |
16 | USM | 46 | 25 |
17 | Yale | 28 | 28 |
Total | 505 | 530 | |
1035 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.51 | 0.56 | 0.6 |
2 | SVM | 0.45 | 0.4 | 0.36 |
3 | LD | 0.49 | 0.51 | 0.54 |
4 | RF | 0.54 | 0.48 | 0.51 |
5 | DT | 0.31 | 0.34 | 0.29 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.62 | 0.59 | 0.63 |
2 | SVM | 0.53 | 0.59 | 0.62 |
3 | LD | 0.51 | 0.58 | 0.6 |
4 | RF | 0.56 | 0.49 | 0.48 |
5 | DT | 0.5 | 0.49 | 0.5 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.68 | 0.63 | 0.58 |
2 | SVM | 0.65 | 0.68 | 0.7 |
3 | LD | 0.7 | 0.61 | 0.62 |
4 | RF | 0.61 | 0.63 | 0.58 |
5 | DT | 0.59 | 0.51 | 0.6 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.73 | 0.58 | 0.65 |
2 | SVM | 0.75 | 0.65 | 0.68 |
3 | LD | 0.73 | 0.71 | 0.69 |
4 | RF | 0.69 | 0.53 | 0.46 |
5 | DT | 0.63 | 0.51 | 0.8 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.78 | 0.69 | 0.7 |
2 | SVM | 0.79 | 0.75 | 0.74 |
3 | LD | 0.74 | 0.7 | 0.67 |
4 | RF | 0.65 | 0.51 | 0.59 |
5 | DT | 0.62 | 0.58 | 0.61 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.73 | 0.65 | 0.74 |
2 | SVM | 0.74 | 0.71 | 0.72 |
3 | LD | 0.7 | 0.69 | 0.68 |
4 | RF | 0.61 | 0.6 | 0.63 |
5 | DT | 0.6 | 0.61 | 0.5 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.6 | 0.61 | 0.65 |
2 | SVM | 0.7 | 0.69 | 0.73 |
3 | LD | 0.62 | 0.64 | 0.61 |
4 | RF | 0.5 | 0.56 | 0.59 |
5 | DT | 0.36 | 0.39 | 0.4 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR | 0.51 | 0.55 | 0.52 |
2 | SVM | 0.51 | 0.55 | 0.52 |
3 | LD | 0.5 | 0.48 | 0.51 |
4 | RF | 0.31 | 0.29 | 0.33 |
5 | DT | 0.33 | 0.32 | 0.3 |
Sr No. | Classifier Name | Max Cumulative Feature Step | Features Count |
---|---|---|---|
1 | LR | 5 | 256 |
2 | RF | 2 | 103 |
3 | DT | 1 | 52 |
4 | SVM | 3 | 154 |
5 | LD | 3 | 154 |
Sr No. | Classifier Name | Accuracy | Sensitivity | Specificity |
---|---|---|---|---|
1 | LR (Ours) | 0.82 | 0.83 | 0.84 |
2 | LD (Ours) | 0.82 | 0.83 | 0.88 |
3 | SVM (Ours) | 0.81 | 0.80 | 0.92 |
4 | RF (Ours) | 0.70 | 0.67 | 0.80 |
5 | DT (Ours) | 0.57 | 0.60 | 0.64 |
6 | Heinsfeld et al., 2018 [44] | 0.63 | 0.58 | 0.67 |
7 | Taban Eslami et al., 2019 [48] | 0.67 | 0.63 | 0.71 |
8 | Ke Niu et al., 2020 [50] | 0.73 | 0.74 | 0.71 |
9 | Zeinab et al., 2020 [51] | 0.70 | 0.77 | 0.61 |
Sr | Site | SVM (Ours) | RF (Ours) | DT (Ours) | LR (Ours) | LD (Ours) | Heinsfeld et al., 2018 [44] | Taban Eslami et al., 2019 [48] | Ke Niu et al., 2020 [50] | Zeinab et al., 2020 (5 fold) [51] |
---|---|---|---|---|---|---|---|---|---|---|
1 | Caltech | 0.83 | 0.65 | 0.53 | 0.78 | 0.67 | 0.52 | 0.52 | 0.66 | 0.54 |
2 | CMU | 0.84 | 0.7 | 0.6 | 0.71 | 0.6 | 0.45 | 0.68 | 0.63 | 0.7 |
3 | KKI | 0.62 | 0.6 | 0.58 | 0.66 | 0.6 | 0.58 | 0.69 | 0.72 | |
4 | Leuven | 0.66 | 0.55 | 0.63 | 0.63 | 0.76 | 0.51 | 0.61 | 0.62 | 0.65 |
5 | MaxMun | 0.59 | 0.49 | 0.48 | 0.61 | 0.47 | 0.54 | 0.48 | 0.46 | |
6 | NYU | 0.78 | 0.69 | 0.6 | 0.78 | 0.64 | 0.64 | 0.68 | 0.7 | 0.65 |
7 | OHSU | 0.5 | 0.5 | 0.6 | 0.74 | 0.69 | 0.74 | 0.82 | 0.57 | |
8 | Olin | 0.52 | 0.59 | 0.7 | 0.7 | 0.7 | 0.44 | 0.65 | 0.58 | |
9 | Pitt | 0.75 | 0.69 | 0.51 | 0.78 | 0.72 | 0.59 | 0.67 | 0.69 | 0.69 |
10 | SBL | 0.66 | 0.63 | 0.56 | 0.66 | 0.59 | 0.46 | 0.51 | 0.56 | |
11 | SDSU | 0.61 | 0.64 | 0.61 | 0.69 | 0.69 | 0.63 | 0.63 | 0.69 | 0.75 |
12 | Stanford | 0.69 | 0.69 | 0.66 | 0.71 | 0.58 | 0.48 | 0.64 | 0.61 | 0.48 |
13 | Trinity | 0.46 | 0.66 | 0.5 | 0.52 | 0.63 | 0.61 | 0.54 | 0.69 | 0.61 |
14 | UCLA | 0.69 | 0.69 | 0.46 | 0.77 | 0.66 | 0.57 | 0.73 | 0.75 | 0.69 |
15 | UM | 0.7 | 0.71 | 0.52 | 0.71 | 0.63 | 0.62 | 0.68 | 0.68 | 0.66 |
16 | USM | 0.71 | 0.76 | 0.76 | 0.8 | 0.74 | 0.57 | 0.63 | 0.8 | 0.77 |
17 | Yale | 0.75 | 0.6 | 0.49 | 0.8 | 0.61 | 0.53 | 0.63 | 0.69 | 0.69 |
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Khan, N.A.; Waheeb, S.A.; Riaz, A.; Shang, X. A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder. Brain Sci. 2020, 10, 754. https://doi.org/10.3390/brainsci10100754
Khan NA, Waheeb SA, Riaz A, Shang X. A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder. Brain Sciences. 2020; 10(10):754. https://doi.org/10.3390/brainsci10100754
Chicago/Turabian StyleKhan, Naseer Ahmed, Samer Abdulateef Waheeb, Atif Riaz, and Xuequn Shang. 2020. "A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder" Brain Sciences 10, no. 10: 754. https://doi.org/10.3390/brainsci10100754