Smart IoT-enabled Healthcare Systems: Real-time Anomaly Detection and Decision Support using Deep Learning Models
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Abstract
Smart healthcare systems that use Internet of Things (IoT) technologies are changing the medical field by letting data about patients be monitored and analyzed in real time. This paper suggests a new way to improve these kinds of systems by adding deep learning models to help find problems and make decisions.IoT devices are used in the suggested system to gather real-time information about things like vital signs, patient behavior, and surrounding factors. Deep learning algorithms, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are then used to handle this data and look for changes in the patient's health that aren't normal. These strange things could be signs of possible health problems or accidents, which would require quick action.Deep learning models are also trained on big datasets to find trends and connections in the data. This lets them help healthcare workers make decisions. For instance, the system can figure out how likely it is that a patient will get a certain illness by looking at their present health and their medical background.One of the best things about this method is that it can change and get better over time. More information is put into the models over time, making them more accurate and good at finding problems and giving useful information. This makes the method very useful for keeping an eye on people with long-term illnesses or finding diseases early. In adding deep learning models to healthcare systems that are connected to the internet of things (IoT) is a useful way to make patient care better. These tools could save lives and improve health by finding problems and helping people make decisions in real time.
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References
Ali, O.; Abdelbaki, W.; Shrestha, A.; Elbasi, E.; Alryalat, M.A.A.; Dwivedi, Y.K. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J. Innov. Knowl. 2023, 8, 100333.
Ghosh, A.M.; Halder, D.; Hossain, S.A. Remote health monitoring system through IoT. In Proceedings of the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 13–14 May 2016; pp. 921–926.
Valsalan, P.; Baomar, T.A.B.; Baabood, A.H.O. IoT based health monitoring system. J. Crit. Rev. 2020, 7, 739–743. [Google Scholar]
Durán-Vega, L.A.; Santana-Mancilla, P.C.; Buenrostro-Mariscal, R.; Contreras-Castillo, J.; Anido-Rifón, L.E.; García-Ruiz, M.A.; Montesinos-López, O.A.; Estrada-González, F. An IoT system for remote health monitoring in elderly adults through a wearable device and mobile application. Geriatrics 2019, 4, 34.
Hamim, M.; Paul, S.; Hoque, S.I.; Rahman, M.N.; Baqee, I.A. IoT based remote health monitoring system for patients and elderly people. In Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 10–12 January 2019; pp. 533–538.
Ali, A.; Almaiah, M.A.; Hajjej, F.; Pasha, M.F.; Fang, O.H.; Khan, R.; Teo, J.; Zakarya, M. An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network. Sensors 2022, 22, 572.
Almaiah, M.A.; Hajjej, F.; Ali, A.; Pasha, M.F.; Almomani, O. A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 2022, 22, 1448.
Sujith, A.; Sajja, G.S.; Mahalakshmi, V.; Nuhmani, S.; Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci. Inform. 2022, 2, 100028.
Ghayvat, H.; Pandya, S.; Patel, A. Deep learning model for acoustics signal based preventive healthcare monitoring and activity of daily living. In Proceedings of the 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India, 28–29 February 2020; pp. 1–7
Cimtay, Y.; Ekmekcioglu, E. Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition. Sensors 2020, 20, 2034.
Maier, M.; Elsner, D.; Marouane, C.; Zehnle, M.; Fuchs, C. DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Montreal, QC, Canada, 13–17 May 2019; pp. 2108–2110.
Pacheco, A.G.; Krohling, R.A. An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification. IEEE J. Biomed. Health Inform. 2021, 25, 3554–3563.
Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare 2020, 8, 348.
Aldahiri, A.; Alrashed, B.; Hussain, W. Trends in using IoT with machine learning in health prediction system. Forecasting 2021, 3, 181–206.
Tiwari, S.; Jain, A.; Sapra, V.; Koundal, D.; Alenezi, F.; Polat, K.; Alhudhaif, A.; Nour, M. A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model. Expert Syst. Appl. 2023, 213, 118933.
Ed-Driouch, C.; Mars, F.; Gourraud, P.A.; Dumas, C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine Intelligence. Sensors 2022, 22, 8313.
Botros, J.; Mourad-Chehade, F.; Laplanche, D. CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals. Sensors 2022, 22, 9190.
Chandrasekhar, N.; Peddakrishna, S. Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes 2023, 11, 1210.
Mirjalali, S.; Peng, S.; Fang, Z.; Wang, C.H.; Wu, S. Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of COVID-19. Adv. Mater. Technol. 2022, 7, 2100545.
Hammad, M.; Abd El-Latif, A.A.; Hussain, A.; Abd El-Samie, F.E.; Gupta, B.B.; Ugail, H.; Sedik, A. Deep learning models for arrhythmia detection in IoT healthcare applications. Comput. Electr. Eng. 2022, 100, 108011.
Nancy, A.A.; Ravindran, D.; Raj Vincent, P.D.; Srinivasan, K.; Gutierrez Reina, D. Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics 2022, 11, 2292.
Haq, A.u.; Li, J.P.; Khan, S.; Alshara, M.A.; Alotaibi, R.M.; Mawuli, C. DACBT: Deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment. Sci. Rep. 2022, 12, 15331.