The Age of the Meta-Doctor: Diagnosing Parkinson’s Disease with Artificial Intelligence and Speech
DOI:
https://doi.org/10.47611/jsrhs.v12i2.4379Keywords:
Artificial intelligence, Parkinson's disease, Deep learning, Basal ganglia, Striatum, Substantia nigra, TensorFlow, Speech disorders, Diagnostic toolAbstract
The basal ganglia consist of the striatum, substantia nigra, and other nuclei, forming various pathways of motor initiation. Parkinson’s disease (PD) is a neurodegenerative disorder characterized by dysfunction of the basal ganglia pathways. Consequently, PD affects the production of speech. An AI model can analyze audio samples from regular and PD patients. A simple deep learning model with various layers, ReLU activation, sigmoid activation, optimizer, loss function, and Early_Stopping can use extracted speech features to classify patients as regular or PD-afflicted with up to 97% accuracy. Overall, the advent of user-friendly artificial intelligence has led to exciting times, with new medical advancements emerging day after day; perhaps the ease of AI implementation will encourage others to solve everyday problems with just a computer and a dream.
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Akbarzadeh-t, M.-R., Azadi, H., Shoeibi, A., & Kobravi, H. (2021). Evaluating the effect of Parkinson's disease on jitter and shimmer speech features. Advanced Biomedical Research, 10(1), 54. https://doi.org/10.4103/abr.abr_254_21
Allamy, H. K. J., & Khan, R. Z. (2015). Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Computer Science, Communication & Instrumentation Devices. https://www.researchgate.net/publication/295198699_METHODS_TO_AVOID_OVER-FITTING_AND_UNDER-FITTING_IN_SUPERVISED_MACHINE_LEARNING_COMPARATIVE_STUDY
Beitz, J. M. (2014). Parkinson's disease: a review. Frontiers in Bioscience, S6(1), 65-74. https://doi.org/10.2741/S415
Bhanja, S., & Das, A. (2018). Impact of data normalization on deep neural network for time series forecasting. ArXiv. https://doi.org/10.48550/arXiv.1812.05519
Chen, Z., Li, G., & Liu, J. (2020). Autonomic dysfunction in Parkinson's disease: Implications for pathophysiology, diagnosis, and treatment. Neurobiology of Disease, 134, 104700. https://doi.org/10.1016/j.nbd.2019.104700
Chollet, F., & others. (2015). Keras [Computer software]. GitHub. https://github.com/fchollet/keras
Cordasco, I. S., Lukasa, & Prewitt, N. (n.d.). Requests (Version 2.28.2) [Computer software]. https://pypi.org/project/requests/
Ellens, D. J., & Leventhal, D. K. (2013). Review: Electrophysiology of basal ganglia and cortex in models of Parkinson disease. Journal of Parkinson's Disease, 3(3), 241-254. https://doi.org/10.3233/JPD-130204
Grossi, E., & Buscema, M. (2007). Introduction to artificial neural networks. European Journal of Gastroenterology & Hepatology, 19(12), 1046-1054. https://doi.org/10.1097/MEG.0b013e3282f198a0
Gupta, S., & Gupta, A. (2019). Dealing with noise problem in machine learning data-sets: A systematic review. Procedia Computer Science, 161, 466-474. https://doi.org/10.1016/j.procs.2019.11.146
Heaton, J. (2017). An empirical analysis of feature engineering for predictive modeling. ArXiv. https://doi.org/10.48550/arXiv.1701.07852
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. ArXiv. https://doi.org/10.48550/arXiv.1412.6980
Kreitzer, A. C., & Malenka, R. C. (2008). Striatal plasticity and basal ganglia circuit function. Neuron, 60(4), 543-554. https://doi.org/10.1016/j.neuron.2008.11.005
Lanciego, J. L., Luquin, N., & Obeso, J. A. (2012). Functional neuroanatomy of the basal ganglia. Cold Spring Harbor Perspectives in Medicine, 2(12), a009621. https://doi.org/10.1101/cshperspect.a009621
Little, M.A., Mcsharry, P.E., Hunter, E.J., Spielman, J., & Ramig, L.O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 56(4), 1015-1022. https://doi.org/10.1109/TBME.2008.2005954
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, … Xiaoqiang Zheng. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved January 10, 2023, from https://www.tensorflow.org/
Nguyen, Q. H., Ly, H.-B., Ho, L. S., Al-ansari, N., Le, H. Van, Tran, Van Q., Prakash, I., & Pham, B. T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021, 1-15. https://doi.org/10.1155/2021/4832864
The Pandas Development Team. (n.d.). Pandas (Version 1.5.3) [Computer software]. https://zenodo.org/record/7658911#.Y_mkwS9Oni0
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2012, January 2). Scikit-learn: Machine learning in python. ArXiv. https://doi.org/10.48550/arXiv.1201.0490
Prechelt, L. (n.d.). Early stopping — but when? Lecture Notes in Computer Science, 1524. https://link.springer.com/chapter/10.1007/3-540-49430-8_3
Scikit-learn Developers. (n.d.). Scikit-learn (Version 1.2.1) [Computer software]. https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
Scikit-learn Developers. (n.d.). 3.1. Cross-validation: evaluating estimator performance. Retrieved January 10, 2023, from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
Stoessl, A. J., Lehericy, S., & Strafella, A. P. (2014). Imaging insights into basal ganglia function, Parkinson's disease, and dystonia. The Lancet, 384(9942), 532-544. https://doi.org/10.1016/S0140-6736(14)60041-6
Szandała, T. (2020). Review and comparison of commonly used activation functions for deep neural networks. ArXiv. https://doi.org/10.48550/arXiv.2010.09458
TensorFlow Developers. (n.d.). TensorFlow (Version 2.11) [Computer software]. https://zenodo.org/record/7641790#.Y_ml-S9Oni0
TensorFlow Developers. (n.d.). Tf.keras.activations.relu. Retrieved January 22, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu
TensorFlow Developers. (n.d.). Tf.keras.activations.sigmoid. Retrieved January 20, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid
TensorFlow Developers. (n.d.). Tf.keras.callbacks.EarlyStopping. Retrieved January 20, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
TensorFlow Developers. (n.d.). Tf.keras.Input. Retrieved January 22, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/Input
TensorFlow Developers. (n.d.). Tf.keras.layers.Dropout. Retrieved January 19, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout
TensorFlow Developers. (n.d.). Tf.keras.losses.BinaryCrossentropy. Retrieved January 22, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy
TensorFlow Developers. (n.d.). Tf.keras.metrics.BinaryAccuracy. Retrieved January 20, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryAccuracy
TensorFlow Developers. (n.d.). Tf.keras.Model. Retrieved January 15, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/Model
TensorFlow Developers. (n.d.). Tf.keras.optimizers.Adam. Retrieved January 19, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam
TensorFlow Developers. (n.d.). Tf.keras.Sequential. Retrieved January 17, 2023, from https://www.tensorflow.org/api_docs/python/tf/keras/Sequential
VanRossum, G., & Drake, F. L. (2010). The Python language reference (3rd ed.). Python Software Foundation.
Verdonck, T., Baesens, B., Óskarsdóttir, M., & Vanden Broucke, S. (2021). Special issue on feature engineering editorial. Machine Learning. https://doi.org/10.1007/s10994-021-06042-2
Wu, T., & Hallett, M. (2013). The cerebellum in Parkinson's disease. Brain, 136(3), 696-709. https://doi.org/10.1093/brain/aws360
Yin, H. H. (2016). The basal ganglia in action. The Neuroscientist, 23(3), 299-313. https://doi.org/10.1177/1073858416654115
Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168, 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
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Copyright (c) 2023 Ayush Tripathi; Dr. Rajagopal Appavu, Jothsna Kethar
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