Real-Time Heart Failure Prediction: An Approach for Ambient Assisted Living
Resumo
Context: The integration of IoT in healthcare has enhanced the capability of Ambient Assisted Living by enabling reliable real-time health monitoring, particularly for heart failure identification. Predictive models play a crucial role in identifying potential heart failures, improving patient outcomes through continuous monitoring and analysis. Problem: Traditional predictive models rely on centralized servers, facing issues like network latency, disruptions, and data overload. These challenges hinder real-time health data processing, limiting their ability to provide timely heart failure risk predictions. Solution: This study proposes a framework that embeds machine learning models directly into mobile devices, leveraging edge computing for real-time heart failure risk assessment. By processing data locally, the solution reduces latency, enhances reliability, and ensures greater data privacy while maintaining predictive accuracy.IS Theory: The framework aligns with socio-technical IS theory by integrating technical innovations and user-centric needs. Embedding predictive models into mobile devices enhances real-time predictions and patient care, bridging technology and human interaction. Method: A heart failure prediction dataset was utilized, employing supervised classification algorithms—Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. Data preprocessing included handling missing values, feature scaling, and encoding. The trained models were deployed on mobile devices using ONNX Runtime for efficient real-time inference. Summary of Results: The proposed system successfully performed real-time heart failure risk prediction on mobile devices, achieving competitive accuracy. Random Forest outperformed other models, reaching an accuracy of 85.33%, demonstrating its effectiveness in edge computing environments. The approach significantly mitigates the connectivity and latency challenges of centralized systems while enhancing data security. Impact in the IS Area: This research highlights the potential of edge computing to enhance real-time healthcare applications by reducing reliance on cloud infrastructure. Future works includes conducting real-environment patient evaluations to validate the system’s clinical applicability, integrating biomedical sensors, improving predictive models using deep learning, and exploring federated learning for privacy-preserving model training.
Referências
American Diabetes Association. 2020. Standards of Medical Care in Diabetes— 2020. Clinical Diabetes 38, 1 (2020), 10–17. DOI: 10.2337/cd20-0062
S. Bebortta and et al. 2023. FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-based Electronic Health Records. Diagnostics 13, 20 (2023), 3166. DOI: 10.3390/diagnostics13203166
L. Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5–32. DOI: 10.1023/A:1010933404324
D. R. Cox. 1958. The Regression Analysis of Binary Sequences. Journal of the Royal Statistical Society: Series B (Methodological) 20, 2 (1958), 215–242.
A. Cuevas-Chávez and et al. 2023. A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases. Healthcare 11, 16 (2023), 2240. DOI: 10.3390/healthcare11162240
ONNX Runtime developers. 2021. ONNX Runtime. [link]. Version: x.y.z.
E. Fix and J. L. Hodges. 1951. Discriminatory Analysis. Technical Report Technical Report 4, Project 21-49-004. USAF School of Aviation Medicine, Randolph Field, Texas. Nonparametric Discrimination: Consistency Properties.
M. I. A. Hossain, A. Tabassum, and Z. U. Shamszaman. 2023. Deep Edge Intelligence-based Solution for Heart Failure Prediction in Ambient Assisted Living. Discover Internet of Things 3, 11 (2023), 43. DOI: 10.1007/s43926-023-00043-4
L. Quaranta, F. Calefato, and F. Lanubile. 2021. KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle. International Conference on Mining Software Repositories (MSR) 18 (2021), 550–554.
R. Lopez Roman and M. Mambo. 2018. Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges. Future Generation Computer Systems 78 (2018), 680–698. DOI: 10.1016/j.future.2016.11.009
M. Satyanarayanan. 2017. The Emergence of Edge Computing. Computer 50, 1 (2017), 30–39. DOI: 10.1109/MC.2017.9
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal 3, 5 (2016), 637–646. DOI: 10.1109/JIOT.2016.2579198
A.-T. Shumba and et al. 2023. Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects. Sensors 23, 15 (2023), 6896. DOI: 10.3390/s23156896
A. Singh and K. Chatterjee. 2023. Edge computing based secure health monitoring framework for electronic healthcare system. Cluster Computing 26 (2023), 1205–1220. DOI: 10.1007/s10586-022-03717-w
P. Spinosa. 2023. Implantação de modelos de aprendizado de máquina no formato ONNX utilizando diferentes frameworks. Universidade Federal do Ceará 1 (2023), 46. Trabalho de Conclusão de Curso.
N. J. Stone, J. G. Robinson, A. H. Lichtenstein, C. N. Bairey Merz, C. B. Blum, R. H. Eckel, A. C. Goldberg, D. Gordon, D. Levy, D. M. Lloyd-Jones, P. McBride, J. S. Schwartz, S. T. Shero, S. C. Jr Smith, K. Watson, and P. W. F. Wilson. 2014. ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults. Circulation 129, Suppl 2 (2014), S1–S45. DOI: 10.1161/01.cir.0000437738.63853.7a
S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya. 2020. HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments. Future Generations Computer Systems 104 (2020), 187–200. DOI: 10.1016/j.future.2019.10.043
X. Wang and et al. 2020. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. IEEE Communications Surveys & Tutorials 22, 2 (2020), 869–904. DOI: 10.1109/COMST.2020.2974448
Q. Yang, Y. Liu, T. Chen, and Y. Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 12:1–12:19. DOI: 10.1145/3298981
S. Yi and et al. 2015. Security and Privacy Issues of Fog Computing: A Survey. In Lecture Notes in Computer Science. Springer International Publishing, Cham, 685–695. DOI: 10.1007/978-3-319-21837-3_6
Y. Zhao and et al. 2021. Edge Intelligence for Internet of Things in Healthcare: A Survey. IEEE Access 9 (2021), 45 – 59. DOI: 10.1109/ACCESS.2020.3045115
