Smart Healthcare Wearable Device for Early Disease Detection Using Machine Learning

Authors

  • Suraj S. Damre Assistant Professor, Department of Information Technology, DYP College of Engineering, Akurdi, Pune, India.
  • Bhagyashree D. Shendkar Assistant Professor, Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Loni, Pune, India.
  • Nikita Kulkarni Associate Professor, Department of Computer Engineering, K J College of Engineering and Management Research, Pune, India.
  • Pankaj R. Chandre Associate Professor, Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Loni, Pune, India.
  • Sayalee Deshmukh Assistant Professor, Department of Computer Science and Engineering (AI & ML), Pimpri Chinchwad College of Engineering, Akurdi, Pune, India.

Keywords:

Smart Wearable Device, Early Disease Detection, Machine Learning, Health Monitoring, Vital Signs, Anomaly Detection

Abstract

The present invention introduces a breakthrough in healthcare technology by unveiling a novel Smart Healthcare Wearable Device for Early Disease Detection using advanced Machine Learning algorithms. This wearable device seamlessly integrates cutting-edge sensor technology, real-time data analytics, and intelligent machine learning models to empower individuals with early detection capabilities for potential health issues. The Smart Healthcare Wearable Device is equipped with a diverse array of sensors, collecting an array of vital health metrics such as heart rate, blood pressure, oxygen saturation, and body temperature. These sensors continuously monitor the wearer's physiological parameters, enabling real-time data acquisition for comprehensive health insights. The innovation lies in the implementation of machine learning algorithms that harness the collected data to recognize subtle deviations from baseline health patterns. These algorithms, trained on vast datasets, exhibit the capacity to identify early indicators of diseases and health anomalies, even before overt symptoms manifest. The machine learning models continuously evolve through an adaptive learning process, enabling the device to tailor its detection capabilities to each individual user. Upon detecting potential health concerns, the Smart Healthcare Wearable Device employs an alert mechanism, immediately notifying the wearer and authorized healthcare providers. This swift alert system enables timely medical intervention, potentially circumventing disease progression and improving treatment outcomes. Furthermore, the device enriches user experience through personalized health recommendations. Leveraging the data-driven insights provided by the machine learning models, the wearable offers activity suggestions, sleep optimization strategies, and dietary advice, promoting proactive wellness management. The Smart Healthcare Wearable Device for Early Disease Detection addresses a critical need for preventive healthcare solutions in an era where early intervention is pivotal. By merging sensor technology and machine learning prowess, this invention introduces a transformative paradigm in healthcare, ultimately enhancing users' quality of life and well-being.

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References

M. Nasr, M. M. Islam, S. Shehata, F. Karray, and Y. Quintana, “Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects,” IEEE Access, vol. 9, pp. 145248–145270, 2021, doi: 10.1109/ACCESS.2021.3118960.

F. Sabry, T. Eltaras, W. Labda, K. Alzoubi, and Q. Malluhi, “Machine Learning for Healthcare Wearable Devices: The Big Picture,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/4653923.

P. R. Chandre, P. N. Mahalle, and G. R. Shinde, “Machine learning based novel approach for intrusion detection and prevention system: a tool based verification,” in 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), Nov. 2018, pp. 135–140, doi: 10.1109/GCWCN.2018.8668618.

A. V. L. N. Sujith, G. S. Sajja, V. Mahalakshmi, S. Nuhmani, and B. Prasanalakshmi, “Systematic review of smart health monitoring using deep learning and Artificial intelligence,” Neurosci. Informatics, vol. 2, no. 3, p. 100028, 2022, doi: 10.1016/j.neuri.2021.100028.

Y. Liao, C. Thompson, S. Peterson, J. Mandrola, and M. S. Beg, “The Future of Wearable Technologies and Remote Monitoring in Health Care,” Am. Soc. Clin. Oncol. Educ. B., no. 39, pp. 115–121, 2019, doi: 10.1200/edbk_238919.

Y. Djenouri, A. Belhadi, A. Yazidi, G. Srivastava, and J. C. W. Lin, “Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism,” Expert Syst., no. March, pp. 1–13, 2022, doi: 10.1111/exsy.13093.

J. Lim, “A smart healthcare-based system for classification of dementia using deep learning,” Digit. Heal., vol. 8, 2022, doi: 10.1177/20552076221131667.

C. Veeraprakashkumar and T. S. Baskaran, “Survey on Various Iot Healthcare Monitoring and Prediction System Using Machine Learning Concepts,” J. Pharm. Negat. Results, vol. 13, no. 9, pp. 2767–2775, 2022, doi: 10.47750/pnr.2022.13.S09.332.

P. Chandre, P. Mahalle, and G. Shinde, “Intrusion prevention system using convolutional neural network for wireless sensor network,” IAES Int. J. Artif. Intell., vol. 11, no. 2, pp. 504–515, 2022, doi: 10.11591/ijai.v11.i2.pp504-515.

M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad, “Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review,” Sensors, vol. 23, no. 2, pp. 1–36, 2023, doi: 10.3390/s23020828.

A. K. Munnangi, S. UdhayaKumar, V. Ravi, R. Sekaran, and S. Kannan, “Survival study on deep learning techniques for IoT enabled smart healthcare system,” Health Technol. (Berl)., vol. 13, no. 2, pp. 215–228, 2023, doi: 10.1007/s12553-023-00736-4.

S. T. Himi, N. T. Monalisa, M. D. Whaiduzzaman, A. Barros, and M. S. Uddin, “MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using Machine Learning,” IEEE Access, vol. 11, no. February, pp. 12342–12359, 2023, doi: 10.1109/ACCESS.2023.3236002.

P. R. Chandre, “Intrusion Prevention Framework for WSN using Deep CNN,” vol. 12, no. 6, pp. 3567–3572, 2021.

M. M. Raza, K. P. Venkatesh, and J. C. Kvedar, “Intelligent risk prediction in public health using wearable device data,” npj Digit. Med., vol. 5, no. 1, 2022, doi: 10.1038/s41746-022-00701-x.

J. D. Huang, J. Wang, E. Ramsey, G. Leavey, T. J. A. Chico, and J. Condell, “Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review,” Sensors, vol. 22, no. 20, 2022, doi: 10.3390/s22208002.

H. Okemiri, A. Uzoma, and N. Christopher, “Internet of Things based Framework for Smart Healthcare Using Hybrid Machine Learning,” 2022, [Online]. Available: http://dx.doi.org/10.21203/rs.3.rs-1428239/v1.

F. Jiang et al., “Artificial intelligence in healthcare: Past, present and future,” Stroke Vasc. Neurol., vol. 2, no. 4, pp. 230–243, 2017, doi: 10.1136/svn-2017-000101.

M. N. Chawla, “AI, IOT and Wearable Technology for Smart Healthcare-A Review,” Int. J. Recent Res. Asp., vol. 7, no. 1, pp. 9–13, 2020.

M. M. El Khatib and G. Ahmed, “Management of artificial intelligence enabled smart wearable devices for early diagnosis and continuous monitoring of CVDS,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 1, pp. 1211–1215, 2019, doi: 10.35940/ijitee.L3108.119119.

J. Wan et al., “Wearable IoT enabled real-time health monitoring system,” Eurasip J. Wirel. Commun. Netw., vol. 2018, no. 1, 2018, doi: 10.1186/s13638-018-1308-x.

A. Shah, S. Ahirrao, S. Phansalkar, and K. Kotecha, “Survey on: Applications of Smart Wearable Technology in Health Insurance,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1042, no. 1, p. 012025, 2021, doi: 10.1088/1757-899x/1042/1/012025.

V.S. Kore, B.A. Tidke and P. Chandre, "Survey of image retrieval techniques and algorithms for image-rich information networks", International Journal of Computer Applications, vol. 112, no. 6, pp. 39-42, 2015.

Funde Rahul, Chandre Pankaj "Dynamic cluster head selection to detect gray hole attack using intrusion detection system in MANETs" Proceedings of the Sixth International Conference on Computer and Communication Technology, ACM (2015), pp. 73-77.

Deshpande S, Gujarathi J, Chandre P, Nerkar P. "A comparative analysis of machine deep learning algorithms for intrusion detection in wsn." In: Security Issues and Privacy Threats in Smart Ubiquitous Computing, 2021; pp. 173–193. Springer.

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Published

10.11.2023

How to Cite

Damre, S. S. ., Shendkar, B. D. ., Kulkarni, N. ., Chandre, P. R. ., & Deshmukh, S. . (2023). Smart Healthcare Wearable Device for Early Disease Detection Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 158–166. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3760

Issue

Vol. 12 No. 4s (2024)

Section

Research Article

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