A Hybrid Machine Learning Model for Peripheral Artery Disease Prediction and Real-Time Applications

Authors

  • H. R. Niveditha Department of Electrics and Communication Engineering, PES College of Engineering, Mandya-571401, India
  • S. Anitha Department of Electrics and Communication Engineering, ACS College of Engineering and Communication Engineering, Visvesvaraya Technological University, Belagavi- 590018, India
  • Nataraj Kanathur Ramaswamy Department of Electronics and Communication Engineering, Don Bosco Institute of Technology, Bengaluru-560074, India
  • Rekha Kanathur Ramaswamy Department of Electronics and Communication Engineering, SJB Institute of Technology, Bengaluru 560060, India
  • Srikantaswamy Mallikarjunaswamy Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India
Volume: 15 | Issue: 3 | Pages: 23692-23698 | June 2025 | https://doi.org/10.48084/etasr.10354

Received: 26 January 2025 | Revised: 28 February 2025 | Accepted: 6 March 2025 | Online: 4 June 2025


Corresponding author: Srikantaswamy Mallikarjunaswamy

Abstract

Peripheral Artery Disease (PAD) is a common and serious circulatory problem for which early and definite diagnosis is necessary to prevent further health complications. The existing diagnostic techniques of Ankle-Brachial Index (ABI) and Doppler ultrasound have several disadvantages: the examination is operator-dependent, the processing times are long, and may be inapplicable or have adaptability in complicated cases. The use of Machine Learning (ML) techniques, such as Support Vector Machines and Random Forest, to overcome these issues, may face problems handling real-time application and non-homogeneous data. The research at hand overcomes such challenges by proposing a Hybrid ML Algorithm for Peripheral Artery Prediction (HMAPAP) based on GBM combined with LSTM networks. The proposed method improved the diagnostic accuracy by 0.25%, processing efficiency by 0.20%, and real-time adaptability by 0.30%, of the traditional ML methods.

Keywords:

peripheral artery disease, hybrid machine learning, prediction model, gradient boosting, LSTM, real-time diagnosis, healthcare analytics, hybrid ML

Downloads

Download data is not yet available.

References

C. P. Loizou, E. Kyriacou, M. B. Griffin, A. N. Nicolaides and C. S. Pattichis, "Association of Intima-Media Texture With Prevalence of Clinical Cardiovascular Disease," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 9, pp. 3017-3026, Sept. 2021.

A. D. Jamthikar, D. Gupta, L. E. Mantella, L. Saba, A. M. Johri and J. S. Suri, "Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022, Art no. 2503810.

A. Ragavi Vijayaraj and S. Pasupathi, "Nature Inspired Optimization in Context-Aware-Based Coronary Artery Disease Prediction: A Novel Hybrid Harris Hawks Approach," in IEEE Access, vol. 12, pp. 92635-92651, 2024.

T. Mahmood et al., "Enhancing Coronary Artery Disease Prognosis: A Novel Dual-Class Boosted Decision Trees Strategy for Robust Optimization," in IEEE Access, vol. 12, pp. 107119-107143, 2024.

S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques," in IEEE Access, vol. 7, pp. 81542-81554, 2019.

A. Paskiewicz et al., "Peripheral artery disease and risk of kidney outcomes: The Atherosclerosis Risk in Communities (ARIC) study," Atherosclerosis, vol. 397, Oct. 2024.

S. Nohara et al., "Subsequent risk of cancer among adults with peripheral artery disease in the community: The atherosclerosis risk in communities (ARIC) study," International Journal of Cardiology, vol. 418, Jan. 2025.

B. Li et al., "Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data," Journal of Vascular Surgery, vol. 80, no. 2, pp. 490-497.e1, Aug. 2024.

J. H. Song, R. T. Tomihama, D. Roh, A. Cabrera, A. Dardik, and S. C. Kiang, "Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients," Annals of Vascular Surgery, vol. 107, pp. 48–54, Oct. 2024.

L. Zhang et al., "Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers," Heliyon, vol. 10, no. 2, Jan. 2024.

Y. Ma, L. Zhang, Q. Li, and X. Qin, "Predictive model for novel subtypes of patients undergoing lower extremity amputation for peripheral artery disease: An unsupervised machine learning study," Heliyon, vol. 10, no. 15, Aug. 2024.

M. S. A. Reshan, S. Amin, M. A. Zeb, A. Sulaiman, H. Alshahrani and A. Shaikh, "A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks," in IEEE Access, vol. 11, pp. 121574-121591, 2023.

C. Xiao, Y. Li and Y. Jiang, "Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm," in IEEE Access, vol. 8, pp. 140108-140121, 2020.

C. Ordonez, "Association rule discovery with the train and test approach for heart disease prediction," in IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 2, pp. 334-343, April 2006.

L. -N. Pu, Z. Zhao and Y. -T. Zhang, "Investigation on Cardiovascular Risk Prediction Using Genetic Information," in IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 5, pp. 795-808, Sept. 2012.

B. Trstenjak, D. Donko, and Z. Avdagic, "Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1212–1216, Dec. 2016.

S. Anwar, S. R. Soomro, S. K. Baloch, A. A. Patoli, and A. R. Kolachi, "Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11561–11567, Oct. 2023.

Downloads

How to Cite

[1]
Niveditha, H.R., Anitha, S., Kanathur Ramaswamy, N., Kanathur Ramaswamy, R. and Mallikarjunaswamy, S. 2025. A Hybrid Machine Learning Model for Peripheral Artery Disease Prediction and Real-Time Applications. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 23692–23698. DOI:https://doi.org/10.48084/etasr.10354.

Metrics

Abstract Views: 29
PDF Downloads: 28

Metrics Information

License

Copyright (c) 2025 H. R. Niveditha, S. Anitha, Nataraj Kanathur Ramaswamy, Rekha Kanathur Ramaswamy, Srikantaswamy Mallikarjunaswamy

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.