Monitoring and Diagnosis of Neurodegenerative Diseases through Advanced Sensor Integration and Machine Learning Techniques
DOI:
https://doi.org/10.63503/j.ijaimd.2024.23Keywords:
Neurodegenerative Diseases, Healthcare Monitoring, Machine Learning, Sen-sor Integration, Early Detection, Predictive AnalyticsAbstract
Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis, are complex disorders that present significant challenges in both diagnosis and progression tracking. This paper explores innovative methods of healthcare monitoring for neurodegenerative diseases through the integration of advanced sensor technology with machine learning algorithms. Specifically, this study presents a comparative analysis of two models that leverage sensor data to enhance early detection and provide continuous patient monitoring. By implementing a dual-model approach, we seek to improve diagnostic accu-racy and predictive capabilities, paving the way for more personalized patient care and timely intervention. Extensive simulations were performed to evaluate the efficacy of these models, examining key metrics to compare performance. The results suggest that the integrated approach can offer substantial benefits over traditional diagnostic methods. Key insights from the simulation results are provided to guide future research and implementation in healthcare settings.
References
Vashistha, R., Yadav, D., Chhabra, D., & Shukla, P. (2019). Artificial intelligence integration for neurodegenerative dis-orders. In Leveraging Biomedical and Healthcare Data (pp. 77-89). Academic Press.
Tăuţan, A. M., Ionescu, B., & Santarnecchi, E. (2021). Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artificial intelligence in medicine, 117, 102081.
Erdaş, Ç. B., Sümer, E., & Kibaroğlu, S. (2021). Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomedical Signal Processing and Control, 70, 103069.
Giannakopoulou, K. M., Roussaki, I., & Demestichas, K. (2022). Internet of things technologies and machine learn-ing methods for Parkinson’s disease diagnosis, monitoring and management: a systematic review. Sensors, 22(5), 1799.
Vrahatis, A. G., Skolariki, K., Krokidis, M. G., Lazaros, K., Exarchos, T. P., & Vlamos, P. (2023). Revolutionizing the early detection of Alzheimer’s disease through non-invasive biomarkers: the role of artificial intelligence and deep learning. Sensors, 23(9), 4184.
Romero, L. E., Chatterjee, P., & Armentano, R. L. (2016). An IoT approach for integration of computational intelli-gence and wearable sensors for Parkinson’s disease diagnosis and monitoring. Health and Technology, 6, 167-172.
Shobana, R., Kumar, G., Kumar, S., Kumar, V., Jojo, J., & Revathy, G. (2025). IoT and Machine Learning for Early Prediction of Neurological Disorders. In Impact of Digital Solutions for Improved Healthcare Delivery (pp. 283-302). IGI Global.
Rasool, S., Husnain, A., Saeed, A., Gill, A. Y., & Hussain, H. K. (2023). Harnessing predictive power: exploring the crucial role of machine learning in early disease detection. JURIHUM: Jurnal Inovasi dan Humaniora, 1(2), 302-315.
Gautam, A. (2022). Towards modern-age advanced sensors for the management of neurodegenerative disorders: current status, challenges and prospects. ECS Sensors Plus, 1(4), 042401.
Dhankhar, S., Mujwar, S., Garg, N., Chauhan, S., Saini, M., Sharma, P., ... & Rani, N. (2024). Artificial intelligence in the management of neurodegenerative disorders. CNS & Neurological Disorders-Drug Targets-CNS & Neurological Disorders), 23(8), 931-940.
MOKHTAR-ANNABA, B. A. D. J. I. (2023). A predictive analysis approach for the detection of risk factors in complex diseases (Doctoral dissertation, Badji Mokhtar-Annaba University).
Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022). Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications, 202, 117362.
Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022). Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications, 202, 117362.
Kale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., ... & Koppula, S. (2024). AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling. Ageing Research Reviews, 102497.
Rossini, P. M., Miraglia, F., & Vecchio, F. (2022). Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis. Alzheimer's & Dementia, 18(12), 2699-2706.
Dipietro, L., Eden, U., Elkin-Frankston, S., El-Hagrassy, M. M., Camsari, D. D., Ramos-Estebanez, C., ... & Wagner, T. (2024). Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson’s Disease. Journal of Big Data, 11(1), 155.
Dhahi, T. S., Dafhalla, A. K. Y., Al-Mufti, A. W., Elobaid, M. E., Adam, T., & Gopinath, S. C. (2024). Application of Nanobiosensor Engineering in the Diagnosis of Neurodegenerative Disorders. Results in Engineering, 102790.
