HYPERTENSION PREDICTION USING DEEP LEARNING WITH TRANSFER LEARNING TECHNIQUES
Abstract
Hypertension or high blood pressure is a chronic condition of consistent rise in blood pressure above the identified normal. It significantly increases the risk of cardiovascular diseases when identified at an advanced stage, but when diagnosed and treated early, it reduces the occurrence of life-threatening complications. This research proposes a prediction model using Deep Learning (DL) with Transfer Learning (TL) techniques for early prediction of hypertension. A pre-trained Feed-Forward Deep Neural Network model, initially developed for diabetes prediction using the PIMA diabetes dataset, is fine-tuned for hypertension prediction using the PPG-BP dataset. This approach utilizes the model's ability to transfer learned knowledge, improving accuracy while reducing computational time. The performance of the model is evaluated using accuracy, precision, and recall. It achieved an accuracy of 81.34%.
References
Alzubaidi, Laith., Zhang, Jinglan., Humaidi, A. J., Al-Dujaili, Ayad., Duan, Ye., Al-Shamma, Omran., Santamaría, J., Fadhel, M. A., Al-Amidie, Muthana., & Farhan, Laith. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8 DOI: https://doi.org/10.1186/s40537-021-00444-8
Bani-Salameh, H., Alkhatib, S. M., Abdalla, M., Al-Hami, M., Banat, R., Zyod, H., & Alkhatib, A. J. (2021). Prediction of diabetes and hypertension using multi-layer perceptron neural networks. Https://Doi.Org/10.1142/S1793962321500124, 12(2). https://doi.org/10.1142/S1793962321500124 DOI: https://doi.org/10.1142/S1793962321500124
Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E. H., Mahbub, Z. B., Monir Uddin, M., Khandakar, A., & Reaz, M. B. I. (2020). Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors 2020, Vol. 20, Page 3127, 20(11), 3127. https://doi.org/10.3390/S20113127 DOI: https://doi.org/10.3390/s20113127
Ciampiconi, L., Elwood, A., Leonardi, M., Mohamed, A., & Rozza, A. (2023). A survey and taxonomy of loss functions in machine learning; A survey and taxonomy of loss functions in machine learning.
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022). A review of some techniques for inclusion of domain-knowledge into deep neural networks. Scientific Reports 2022 12:1, 12(1), 1–15. https://doi.org/10.1038/s41598-021-04590-0 DOI: https://doi.org/10.1038/s41598-021-04590-0
Datta, S., Morassi Sasso, A., Kiwit, N., Bose, S., Nadkarni, G., Miotto, R., & Böttinger, E. P. (2022). Predicting hypertension onset from longitudinal electronic health records with deep learning. JAMIA Open, 5(4), 1–10. https://doi.org/10.1093/JAMIAOPEN/OOAC097 DOI: https://doi.org/10.1093/jamiaopen/ooac097
DeGuire, J., Clarke, J., Rouleau, K., Roy, J., & Bushnik, T. (2019). Blood pressure and hypertension. Health Reports, 30(2), 14–21. https://doi.org/10.25318/82-003-x201900200002
Estiko, R. I., Rijanto, E., Juwana, Y. B., Juzar, D. A., & Widyantoro, B. (2024). 73. Hypertension Prediction Models Using Machine Learning with Easy-to-Collect Risk Factors: A Systematic Review. Journal of Hypertension, 42(Suppl 2), e19. https://doi.org/10.1097/01.HJH.0001027072.19895.81 DOI: https://doi.org/10.1097/01.hjh.0001027072.19895.81
Farahani, A., Voghoei, S., Rasheed, K., & Arabnia, H. R. (2021). A Brief Review of Domain Adaptation. 877–894. https://doi.org/10.1007/978-3-030-71704-9_65/COVER DOI: https://doi.org/10.1007/978-3-030-71704-9_65
Feature Importance Explained. What is Feature importance ? | by akhil anand | Analytics Vidhya | Medium. (202 C.E.). https://medium.com/analytics-vidhya/feature-importance-explained-bfc8d874bcf
Goyal, A., Hossain, G., Chatrati, S. P., Bhattacharya, S., Bhan, A., Gaurav, D., & Tiwari, S. M. (2020). Smart Home Health Monitoring System for Predicting Type 2 Diabetes and Hypertension. J. King Saud Univ. Comput. Inf. Sci.
G. Zhang, Z. M. Y. Z. X. M. B. L. D. C. and Y. Z. (2020). A noninvasive blood glucose monitoring system based on smartphone ppg signal processing and machine learning. IEEE Transactions on Industrial Informatics, 7209–7218. DOI: https://doi.org/10.1109/TII.2020.2975222
Herrera-Huisa, L., Arias-Meza, N., & Cabanillas-Carbonell, M. (2021). Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 769–775. https://doi.org/10.1109/ISPA-BDCLOUD-SOCIALCOM-SUSTAINCOM52081.2021.00110 DOI: https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Abdul Azim, M. (2021). Transfer learning: a friendly introduction. https://doi.org/10.1186/s40537-022-00652-w DOI: https://doi.org/10.1186/s40537-022-00652-w
Hypertension. (n.d.). Retrieved September 24, 2024, from https://www.who.int/news-room/fact-sheets/detail/hypertension
Kario, K., Okura, A., Hoshide, S., & Mogi, M. (2024). The WHO Global report 2023 on hypertension warning the emerging hypertension burden in globe and its treatment strategy. Hypertension Research 2024 47:5, 47(5), 1099–1102. https://doi.org/10.1038/s41440-024-01622-w DOI: https://doi.org/10.1038/s41440-024-01622-w
Koshimizu, H., Kojima, R., Kario, K., & Okuno, Y. (2020). Prediction of blood pressure variability using deep neural networks. International Journal of Medical Informatics, 136, 104067. https://doi.org/10.1016/J.IJMEDINF.2019.104067 DOI: https://doi.org/10.1016/j.ijmedinf.2019.104067
Layton, A. T. (2024). AI, Machine Learning, and ChatGPT in Hypertension. Hypertension, 81(4), 709–716. https://doi.org/10.1161/HYPERTENSIONAHA.124.19468/ASSET/21BAE21B-71F2-4AE0-BD29-E8F8452B742E/ASSETS/GRAPHIC/HYPERTENSIONAHA.124.19468.FIG03.JPG DOI: https://doi.org/10.1161/HYPERTENSIONAHA.124.19468
Martinez-Ríos, E., Montesinos, L., Alfaro-Ponce, M., & Pecchia, L. (2021). A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomedical Signal Processing and Control, 68, 102813. https://doi.org/10.1016/J.BSPC.2021.102813 DOI: https://doi.org/10.1016/j.bspc.2021.102813
Mishra, R. K., Reddy, G. Y. S., & Pathak, H. (2021). The Understanding of Deep Learning: A Comprehensive Review. In Mathematical Problems in Engineering (Vol. 2021). Hindawi Limited. https://doi.org/10.1155/2021/5548884 DOI: https://doi.org/10.1155/2021/5548884
Nematollahi, M. A., Jahangiri, S., Asadollahi, A., Salimi, M., Dehghan, A., Mashayekh, M., Roshanzamir, M., Gholamabbas, G., Alizadehsani, R., Bazrafshan, M., Bazrafshan, H., Bazrafshan drissi, H., & Shariful Islam, S. M. (2023). Body composition predicts hypertension using machine learning methods: a cohort study. Scientific Reports 2023 13:1, 13(1), 1–11. https://doi.org/10.1038/s41598-023-34127-6 DOI: https://doi.org/10.1038/s41598-023-34127-6
Nour, M., & Polat, K. (2020). Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. https://doi.org/10.1155/2020/2742781 DOI: https://doi.org/10.1155/2020/2742781
Pan, S. J., & Yang, Q. (2020). A survey on transfer learning. In IEEE Transactions on Knowledge and Data Engineering (Vol. 22, Issue 10, pp. 1345–1359). https://doi.org/10.1109/TKDE.2009.191 DOI: https://doi.org/10.1109/TKDE.2009.191
PPG-BP. (n.d.). Retrieved November 15, 2024, from https://www.kaggle.com/datasets/phamminhhiu/ppgbp
PPG-BP Database. (2021). https://figshare.com/articles/dataset/PPG-BP_Database_zip/5459299
Sablons De Gélis, R. (2021). Transfer learning techniques in time series analysis KTH Master Thesis Report. In DEGREE PROJECT IN TECHNOLOGY.
Shrivastava, A., Chakkaravarthy, M., & Shah, M. A. (2023). A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. Healthcare Analytics, 4, 100219. https://doi.org/10.1016/J.HEALTH.2023.100219 DOI: https://doi.org/10.1016/j.health.2023.100219
Transfer Learning Guide: A Practical Tutorial With Examples for Images and Text in Keras. (2023). Neptune. https://neptune.ai/blog/transfer-learning-guide-examples-for-images-and-text-in-keras
Understanding Transfer Learning for Deep Learning. (2021). https://www.analyticsvidhya.com/blog/2021/10/understanding-transfer-learning-for-deep-learning/
Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., Ramirez, A., Schlaich, M., Stergiou, G. S., Tomaszewski, M., Wainford, R. D., Williams, B., & Schutte, A. E. (2020). 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension, 75(6), 1334–1357. https://doi.org/10.1161/hypertensionaha.120.15026 DOI: https://doi.org/10.1161/HYPERTENSIONAHA.120.15026
Weber-Boisvert, G., Gosselin, B., & Sandberg, F. (2023). Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied. Frontiers in Physiology, 14. https://doi.org/10.3389/FPHYS.2023.1126957 DOI: https://doi.org/10.3389/fphys.2023.1126957
Wu, L., Gao, J., Zhuang, J., Wu, M., Chen, S., Wang, G., Hong, L., Wu, S., & Hong, J. (2023). Hypertension combined with atherosclerosis increases the risk of heart failure in patients with diabetes. Hypertension Research 2023, 1–13. https://doi.org/10.1038/s41440-023-01529-y DOI: https://doi.org/10.1038/s41440-023-01529-y
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Member, S., Xiong, H., & He, Q. (2020). A Comprehensive Survey on Transfer Learning.
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