A Hybrid Machine Learning and Blockchain Architecture for Enhanced ALS Detection
DOI:
https://doi.org/10.13052/jmm1550-4646.2162Keywords:
Amyotrophic Lateral Sclerosis, Machine Learning, CNN-BiLSTM, Blockchain healthcare, Optimism Layer-2, Feature Selection, HealthcareAbstract
The diagnosis of amyotrophic lateral sclerosis (ALS) experiences critical delays averaging 9-12 months, limiting therapeutic interventions. We propose a novel architecture that integrates deep learning with blockchain technology for secure and auditable speech ALS detection. Our CNN-BiLSTM architecture with an attention mechanism processes the acoustic characteristics of 217 participants (133 ALS, 84 controls) in the VOC-ALS and Minsk datasets. The model achieves 96.5% accuracy, 95.3% sensitivity, and 97.8% specificity, outperforming traditional approaches. The blockchain implementation on Optimism Layer-2 ensures data integrity through immutable audit trails, IPFS off-chain storage, and smart contract-governed access control. This hybrid approach addresses both diagnostic accuracy and critical data governance challenges in multi-institutional ALS research, demonstrating feasibility for clinical deployment while maintaining patient privacy and regulatory compliance.
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References
Masrori, P., & Van Damme, P. (2020). Amyotrophic lateral sclerosis: a clinical review. European Journal of Neurology, 27(10), 1918–1929. doi:10.1111/ene.14393
Conde, B., Winck, J. C., & Azevedo, L. F. (2019). Estimating amyotrophic lateral sclerosis and motor neuron disease prevalence in Portugal using a pharmaco-epidemiological approach and a bayesian multiparameter evidence synthesis model. Neuroepidemiology, 53(1–2), 73-83.
Swinnen, B., & Robberecht, W. (2014). The phenotypic variability of amyotrophic lateral sclerosis. Nature Reviews Neurology, 10(11), 661–670. doi:10.1038/nrneurol.2014.184
Mitsumoto, H., Brooks, B. R., & Silani, V. (2014). Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved? Lancet Neurology, 13(11), 1127–1138. doi:10.1016/S1474-4422(14)70129-2
Yunusova, Y., Plowman, E. K., Green, J. R., Barnett, C., & Bede, P. (2019). Clinical measures of bulbar dysfunction in ALS. Frontiers in Neurology, 10, 106. doi:10.3389/fneur.2019.00106
Vashkevich, M., & Rushkevich, Y. (2021). Classification of ALS patients based on acoustic analysis of sustained vowel phonations. Biomedical Signal Processing and Control, 65, 102350. doi:10.1016/j.bspc.2020.102350
Vieira, F. G., Venugopalan, S., Premasiri, A. S., McNally, M., Jansen, A., McCloskey, K., Brenner, M. P., & Perrin, S. (2022). A machine-learning based objective measure for ALS disease severity. NPJ Digital Medicine, 5(1), 45. doi:10.1038/s41746-022-00588-8
Rong, P., Yunusova, Y., Eshghi, M., Rowe, H. P., & Green, J. R. (2020). A speech measure for early stratification of fast and slow progressors of bulbar amyotrophic lateral sclerosis: Lip movement jitter. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 21(1–2), 34–41. doi:10.1080/21678421.2019.1681454
Tena, A., Claria, F., Solsona, F., Meister, E., & Povedano, M. (2021). Detection of bulbar involvement in patients with amyotrophic lateral sclerosis by machine learning voice analysis: Diagnostic decision support development study. JMIR Medical Informatics, 9(3), e21331. doi:10.2196/21331
Wang, L., Gong, Y., Dawalatabad, N., Vilela, M., Placek, K., Tracey, B., … & Glass, J. (2024). Automatic prediction of amyotrophic lateral sclerosis progression using longitudinal speech transformer. arXiv preprint, arXiv:2406.18625.
Stegmann, G. M., Hahn, S., Liss, J., Shefner, J., Rutkove, S., Shelton, K., Duncan, C. J., & Berisha, V. (2020). Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis. npj Digital Medicine, 3(1), 132. doi:10.1038/s41746-020-00335-x
Papaiz, F., Dourado Jr, M. E. T., de Medeiros Valentim, R. A., Pinto, R., de Morais, A. H. F., & Arrais, J. P. (2024). Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis. BMC Med Inform Decis Mak, 24(1), 80. doi:10.1186/s12911-024-02484-5
Pancotti, C., Birolo, G., Rollo, C. et al. Deep learning methods to predict amyotrophic lateral sclerosis disease progression. Scientific Reports, 12(1), 13738. doi:10.1038/s41598-022-17805-9
Turabieh, H., Afshar, A. S., Statland, J., Song, X., & Pooled Resource Open-Access ALS Clinical Trials Consortium. (2024, January). Towards a machine learning empowered prognostic model for predicting disease progression for amyotrophic lateral sclerosis. AMIA Annual Symposium Proceedings, 2023, 718–725.
Barbalho, I. M., Fonseca, A. L., Fernandes, F., Henriques, J., Gil, P., Nagem, D., … & Valentim, R. A. (2023). Digital health solution for monitoring and surveillance of Amyotrophic Lateral Sclerosis in Brazil. Frontiers in Public Health, 11, 1209633. doi:10.3389/fpubh.2023.1209633
Tavazzi, E., Longato, E., Vettoretti, M., Aidos, H., Trescato, I., Roversi, C., … & Alves, I. (2023). Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. Artificial Intelligence in Medicine, 102588. doi:10.1016/j.artmed.2023.102588
Dadu, A., Satone, V., Kaur, R., Hashemi, S. H., Leonard, H., Iwaki, H., … & Faghri, F. (2022). Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts. NPJ Parkinson’s Disease, 8(1), 172.
Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019, April). Blockchain technology in healthcare: a systematic review. In Healthcare (Vol. 7, No. 2, p. 56). MDPI. doi:10.3390/healthcare7020056
Xu, G., Qi, C., Dong, W., Gong, L., Liu, S., Chen, S., Liu, J., & Zheng, X. (2023). A privacy-preserving medical data sharing scheme based on blockchain. IEEE Journal of Biomedical and Health Informatics, 27(2), 698–709. doi:10.1109/JBHI.2022.3203577
Kumar, R., Khan, A. A., Kumar, J., et al. (2021). Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. IEEE Sensors Journal, 21(14), 16301–16314. doi:10.1109/JSEN.2021.3076767
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A. (2019). Privacy-preserving Federated Brain Tumour Segmentation. Machine learning in medical imaging. MLMI (Workshop), 11861, 133–141. doi:10.1007/978-3-030-32692-0_16
Dubbioso, R., Spisto, M., Verde, L., et al. (2024). Voice signals dataset for amyotrophic lateral sclerosis patients and healthy controls. Scientific Data, 11(1), 800. doi:10.1038/s41597-024-03597-2
Brookes, M. VOICEBOX: Speech processing toolbox for Matlab. Department of Electrical & Electronic Engineering. Imperial College, London; 2002.
Jadoul, Y., Thompson, B., & De Boer, B. (2018). Introducing parselmouth: A python interface to praat. Journal of Phonetics, 71, 1–15. doi:10.1016/j.wocn.2018.07.001
Teixeira, J. P., Oliveira, C., & Lopes, C. (2013). Vocal acoustic analysis–jitter, shimmer and hnr parameters. Procedia Technology, 9, 1112–1122. doi:10.1016/j.protcy.2013.12.124
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. doi:10.1111/j.2517-6161.1996.tb02080.x
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. doi:10.1109/TPAMI.2005.159
Wang, L., Gong, Y., Dawalatabad, N., Vilela, M., Placek, K., Tracey, B., Gong, Y., Premasiri, A., Vieira, F., & Glass, J. (2024). Automatic prediction of amyotrophic lateral sclerosis progression using longitudinal speech transformer. In Proceedings of Interspeech 2024 (pp. 2000–2004). doi:10.21437/Interspeech.2024-158
Buterin, V. (2021). An incomplete guide to rollups, 2021. URL: https://vitalik.ca/general/2021/01/05/rollup.html.
Thibault, L., Sarry, T., & Hafid, A. S. (2022). Blockchain scaling using rollups: A comprehensive survey. IEEE Access, 10, 93039–93054. doi:10.1109/ACCESS.2022.3200051
Cebola, R., Folgado, D., Carreiro, A. V., & Gamboa, H. (2023). Speech-Based Supervised Learning Towards the Diagnosis of Amyotrophic Lateral Sclerosis. In Biosignals (pp. 74–85). doi:10.5220/0011694700003414
Farrokhi, Z., Zakavi, S. A., Sarafraz, A., Valifard, M., Yousefzadeh, S., Tafreshi, Z. M., et al. (2025). Acoustic signatures of bulbar ALS: Predictive modeling with sustained vowels and LightGBM. eNeurologicalSci, 40, 100579. doi:10.1016/j.ensci.2024.100579
Louja, A., Jamali, A., & Naja, N. (2024). Blockchain-powered artificial intelligence for healthcare systems data orchestration. In International Conference on Mathematics Data Science (pp. 155–163). Springer.
Louja, A., Drira, I., Jamali, A., Naja, N., & Sliman, L. (2025). Wearable sensor-based eye-rubbing monitoring: a hybrid CNN-attentionrub architecture for keratoconus prevention. Journal of Supercomputing, 81, 1268. https://doi.org/10.1007/s11227-025-07744-3.
Drira, I., Louja, A., Sliman, L., Soler, V., Noor, M., Jamali, A., & Fournie, P. (2024). Eye-rubbing detection tool using artificial intelligence on a smartwatch in the management of keratoconus. Translational Vision Science & Technology, 13(12), 16. https://doi.org/10.1167/tvst.13.12.16.
