A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Materials
3.1.1. Datasets
- A Parkinson’s speech dataset with multiple types of sound recording [23]. This dataset is not as large as the main PD dataset [9] in terms of subject participation, but it contains a total of 1040 samples; we refer to this dataset in our article as the PD dataset. This dataset [23] contains a total of 40 subjects; half of them had PD, and half of them were healthy. Moreover, the group of PD patients has 6 women and 14 men, while the group of non-PD patients has 10 women and 10 men. Each subject contributed 26 different types of voice recordings, ranging from sustained vowels to short sentences. The number of speech features is quite low compared to the main PD dataset [9], i.e., a total of 26 features.
3.1.2. Data Pre-Processing
3.2. Methods
4. Vocal Tab Transformer
- Train XgBoost with the complete dataset.
- Estimate feature importance using the trained XgBoost model.
- Rank the features according to the importance score.
- Select the top N features and train the proposed network
4.1. System Model
4.1.1. Feature Selection
- Booster = gbtree (Gradient Boosting Tree)
- N_estimators = 100
- Learning_rate = 0.3
- Maximum depth of a tree = 6
- Tree_method = auto
- The support vector classifier (SVC) feature score and
- The permutation feature score.
4.1.2. Feature Embedding
4.1.3. Transformer Block
4.2. Architecture and Working
- Feature selection
- Trainable NN model
- Feature Embedder
- Transformer Block
- MLP Head
5. Experimentation and Results
5.1. Experimental Setup and Parameters
5.1.1. Vocal Tab Transformer
- Number of transformer encoders = 6
- Attention head = 1
- Feature embedding dimension = 64
- Learning rate =
- Batch size = 32
- Epoch = 20
5.1.2. MLP
5.1.3. XGBoost and Scikit-Learn’s Classifiers
- colsample_bytree = 0.3
- gamma = 0.0
- learning_rate = 0.2
- max_depth = 10
- min_child_weight = 1
5.2. Results
- A comparison of the proposed solution with other frequently used models such as MLP, Xgboost, and RF.
- Model performance on other datasets [23].
- The proposed approach’s hyper-parameter effect on the AUC score.
- A smaller batch size yields better results; we used 32 data points for the model training to balance the trade-off between training time and accuracy; see Figure 9 (Left).
- The number of attention heads has no significant impact on the performance of the transformer; see Figure 9 (Right). Hence, a single attention head is selected as a default parameter for the experiments.
- Ninety-six seems to be the right number of features to build an accurate model. There is no significant improvement when we increase the number of features is increased beyond that; see Figure 8 (Left).
6. Analysis
7. Conclusions and Future Scope
- A feature selection strategy that works well with the proposed solution using XgBoost.
- A report of a performance comparison of the frequently used ML algorithms, along with our proposed solution;
- A novel approach to embed vocal features in fixed-length vectors using fully connected NN layers;
- A detailed study of the different proposed network parameters and their relevance to the application; and
- Empirical evidence of the stability of the proposed network’s performance with increased depth and a comparative study with respect to MLP, which may lead to a more accurate model once a large sample PD dataset is available.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Main PD Dataset [9] | PD Dataset [23] |
---|---|---|
XgBoost [46] | 0.91432 ± 0.0037 | 0.64649 ± 0.0082 |
Support Vector Classifier [47] | 0.88379 ± 0.0156 | 0.60249 ± 0.0002 |
Permutation [45] | 0.82015 ± 0.1062 | 0.58297 ± 0.0090 |
Model | Training Samples | Avg Training Time | Test Samples | Avg Inference Time | Avg Inference Time per Sample |
---|---|---|---|---|---|
Proposed network (Figure 4) | 678 | 38,860.778 | 78 | 1029.443 | 13.197 |
MLP (Figure 6) | 678 | 2586.155 | 78 | 179.195 | 2.297 |
Xgboost [46] | 678 | 537.893 | 78 | 3.362 | 0.043 |
GradientBoosting [25] | 678 | 15,637.271 | 78 | 0.417 | 0.005 |
AdaBoost [45] | 678 | 3530.252 | 78 | 6.293 | 0.080 |
RandomForest [45] | 678 | 1385.690 | 78 | 8.577 | 0.109 |
DecisionTree [45] | 678 | 848.363 | 78 | 0.309 | 0.003 |
SVM [47] | 678 | 1499.896 | 78 | 23.164 | 0.296 |
KNeighbors [45] | 678 | 1.022 | 78 | 8.181 | 0.104 |
LogisticRegression [45] | 678 | 171.001 | 78 | 0.208 | 0.002 |
GaussianNB [45] | 678 | 4.369 | 78 | 0.682 | 0.008 |
Model | With Feature Selection (96 Features) | Without Feature Selection (753 Features) | ||||
---|---|---|---|---|---|---|
Avg ROC-AUC | Avg Precision | Avg Recall | Avg ROC-AUC | Avg Precision | Avg Recall | |
Proposed network | 0.9143 ± 0.0037 | 0.8819 ± 0.0043 | 0.90378 ± 0.0103 | 0.8574 ± 0.0039 | 0.8234 ± 0.0069 | 0.8421 ± 0.004 |
Xgboost | 0.9028 ± 0.0042 | 0.8634 ± 0.0021 | 0.8693 ± 0.0074 | 0.875 ± 0.0041 | 0.8421 ± 0.0078 | 0.8411 ± 0.0039 |
MLP | 0.8728 ± 0.0039 | 0.8328 ± 0.0095 | 0.8712 ± 0.0068 | 0.82565 ± 0.0130 | 0.8032 ± 0.0094 | 0.7908 ± 0.0124 |
GradientBoosting | 0.9009 ± 0.0008 | 0.8634 ± 0.0021 | 0.8584 ± 0.0083 | 0.87083 ± 0.0024 | 0.8414 ± 0.0028 | 0.841 ± 0.0067 |
AdaBoost | 0.8546 ± 0 | 0.8584 ± 0 | 0.8514 ± 0 | 0.85756 ± 0 | 0.8523 ± 0 | 0.8544 ± 0 |
RandomForest | 0.8939 ± 0.0041 | 0.7981 ± 0.006 | 0.8643 ± 0.0092 | 0.85917 ± 0.0053 | 0.8251 ± 0.0063 | 0.803 ± 0.0067 |
DecisionTree | 0.7456 ± 0.0072 | 0.7213 ± 0.0082 | 0.7749 ± 0.001 | 0.69195 ± 0.0082 | 0.6642 ± 0.0149 | 0.6597 ± 0.0212 |
SVM | 0.8737 ± 0 | 0.8031 ± 0 | 0.8723 ± 0 | 0.80743 ± 0 | 0.7731 ± 0 | 0.7674 ± 0 |
KNeighbors | 0.84047± 0 | 0.8031 ± 0 | 0.8599 ± 0 | 0.7796 ± 0 | 0.7438 ± 0 | 0.7264 ± 0 |
LogisticRegression | 0.83081 ± 0 | 0.8321 ± 0 | 0.794 ± 0 | 0.78466 ± 0 | 0.777 ± 0 | 0.776 ± 0 |
GaussianNB | 0.83593 ± 0 | 0.7816 ± 0 | 0.7943 ± 0 | 0.76863 ± 0 | 0.7422 ± 0 | 0.7374 ± 0 |
Model | With Feature Selection (8 Features) | Without Feature Selection (26 Features) | ||||
---|---|---|---|---|---|---|
Avg ROC-AUC | Avg Precision | Avg Recall | Avg ROC-AUC | Avg Precision | Avg Recall | |
Proposed network | 0.6464 ± 0.0024 | 0.623 ± 0.0035 | 0.6304 ± 0.0027 | 0.6293 ± 0.0081 | 0.6034 ± 0.0023 | 0.6013 ± 0.0018 |
Xgboost | 0.5761 ± 0.0104 | 0.5532 ± 0.0076 | 0.5527 ± 0.0038 | 0.568 ± 0.0021 | 0.5521 ± 0.0076 | 0.562 ± 0.054 |
MLP | 0.6920 ± 0.0062 | 0.6439 ± 0.0103 | 0.6134 ± 0.0089 | 0.6605 ± 0.0040 | 0.6532 ± 0.0061 | 0.6243 ± 0.0095 |
GradientBoosting | 0.5992 ± 0.0003 | 0.542 ± 0.0001 | 0.5565± 0.0003 | 0.5748 ± 0.0014 | 0.5613 ± 0.0009 | 0.5443 ± 0.001 |
AdaBoost | 0.5932 ± 0 | 0.5824 ± 0 | 0.5703 ± 0 | 0.5403 ± 0 | 0.5272 ± 0 | 0.5326 ± 0 |
RandomForest | 0.6086 ± 0.0058 | 0.5554 ± 0.0017 | 0.5472 ± 0.0016 | 0.5737 ± 0.0050 | 0.5523 ± 0.0094 | 0.5145 ± 0.0019 |
DecisionTree | 0.5147 ± 0.0048 | 0.4824 ± 0.0136 | 0.4621 ± 0.0104 | 0.5375 ± 0.0040 | 0.5124 ± 0.0103 | 0.4924 ± 0.0048 |
SVM | 0.6176 ± 0 | 0.5824 ± 0 | 0.5578 ± 0 | 0.5937 ± 0 | 0.5434 ± 0 | 0.5251 ± 0 |
KNeighbors | 0.5953 ± 0 | 0.5627 ± 0 | 0.5936 ± 0 | 0.5836 ± 0 | 0.5421 ± 0 | 0.5738 ± 0 |
LogisticRegression | 0.6307 ± 0 | 0.6131 ± 0 | 0.6014 ± 0 | 0.606 ± 0 | 0.5839 ± 0 | 0.5982 ± 0 |
GaussianNB | 0.5832 ± 0 | 0.6021 ± 0 | 0.5341 ± 0 | 0.5705 ± 0 | 0.5894 ± 0 | 0.5474 ± 0 |
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Nijhawan, R.; Kumar, M.; Arya, S.; Mendirtta, N.; Kumar, S.; Towfek, S.K.; Khafaga, D.S.; Alkahtani, H.K.; Abdelhamid, A.A. A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features. Biomimetics 2023, 8, 351. https://doi.org/10.3390/biomimetics8040351
Nijhawan R, Kumar M, Arya S, Mendirtta N, Kumar S, Towfek SK, Khafaga DS, Alkahtani HK, Abdelhamid AA. A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features. Biomimetics. 2023; 8(4):351. https://doi.org/10.3390/biomimetics8040351
Chicago/Turabian StyleNijhawan, Rahul, Mukul Kumar, Sahitya Arya, Neha Mendirtta, Sunil Kumar, S. K. Towfek, Doaa Sami Khafaga, Hend K. Alkahtani, and Abdelaziz A. Abdelhamid. 2023. "A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features" Biomimetics 8, no. 4: 351. https://doi.org/10.3390/biomimetics8040351
APA StyleNijhawan, R., Kumar, M., Arya, S., Mendirtta, N., Kumar, S., Towfek, S. K., Khafaga, D. S., Alkahtani, H. K., & Abdelhamid, A. A. (2023). A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features. Biomimetics, 8(4), 351. https://doi.org/10.3390/biomimetics8040351