This is a preview and has not been published. View submission

Explainable Machine Learning Algorithms to Predict Cardiovascular Strokes

Authors

  • Afia Fairooz Tasnim Department of Public Health, California State University, Long Beach, USA
  • Rukshanda Rahman Department of Computer Science, Westcliff University, Irvine, USA
  • Mani Prabha Department of Business Administration, International American University, Los Angeles, USA
  • Md. Azad Hossain Department of Business Administration, International American University, Los Angeles, USA
  • Sadia Islam Nilima Department of Business Administration, International American University, Los Angeles, USA
  • Md Abdullah Al Mahmud Department of Business Administration, International American University, Los Angeles, USA
  • Timotei Istvan Erdei Department of Vehicles Engineering, Faculty of Engineering, University of Debrecen, Hungary
Volume: 15 | Issue: 1 | Pages: 20131-20137 | February 2205 | https://doi.org/10.48084/etasr.9152

Abstract

Cardiovascular disease has been more common throughout the past several decades. Cardiovascular disease detection methods use machine learning algorithms to assess data and provide accurate cardiac diagnosis. An accurate and comprehensive assessment of cardiovascular risk is essential to improve cardiovascular protection and reduce the frequency and severity of heart attacks and strokes. This paper proposes a machine learning-based autonomous strategy for the diagnosis of cardiovascular disease. Some preprocessing methods were applied to improve the results and accuracy. Finally, lazy prediction was used to find the best model by applying a neural network and two ensemble models. The best accuracy of 99% was obtained with the HistGradientBoosting (ensemble) classifier, which obtained respectable results with a higher accuracy rate. This model can enhance the ability to predict cardiovascular disease with better accuracy.

Keywords:

cardiovascular, healthcare system, machine learning, ensemble, social development

Downloads

Download data is not yet available.

References

"Cardiovascular diseases," World Health Organization. https://www.who.int/health-topics/cardiovascular-diseases.

A. Khosla, Y. Cao, C. C. Y. Lin, H. K. Chiu, J. Hu, and H. Lee, "An integrated machine learning approach to stroke prediction," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, DC, USA, Jul. 2010, pp. 183–192.

M. W. Ullah, R. Rahman, S. I. Nilima, A. F. Tasnim, and M. B. Aziz, "Health Behaviors and Outcomes of Mobile Health Apps and Patient Engagement in the USA," Journal of Computer and Communications, vol. 12, no. 10, pp. 78–93, 2024.

N. G. B. Amma, "Cardiovascular disease prediction system using genetic algorithm and neural network," in 2012 International Conference on Computing, Communication and Applications, Dindigul, Tamilnadu, India, Feb. 2012, pp. 1–5.

L. T. Braun et al., "Palliative Care and Cardiovascular Disease and Stroke: A Policy Statement From the American Heart Association/American Stroke Association," Circulation, vol. 134, no. 11, Sep. 2016.

L. Soares-Miranda, D. S. Siscovick, B. M. Psaty, W. T. Longstreth, and D. Mozaffarian, "Physical Activity and Risk of Coronary Heart Disease and Stroke in Older Adults: The Cardiovascular Health Study," Circulation, vol. 133, no. 2, pp. 147–155, Jan. 2016.

R. L. Sacco et al., "The Heart of 25 by 25: Achieving the Goal of Reducing Global and Regional Premature Deaths From Cardiovascular Diseases and Stroke: A Modeling Study From the American Heart Association and World Heart Federation," Circulation, vol. 133, no. 23, Jun. 2016.

A. Morgentaler, M. M. Miner, M. Caliber, A. T. Guay, M. Khera, and A. M. Traish, "Testosterone Therapy and Cardiovascular Risk: Advances and Controversies," Mayo Clinic Proceedings, vol. 90, no. 2, pp. 224–251, Feb. 2015.

M. Rahman, Y. Wen, H. Xu, T. L. Tseng, and S. Akundi, "Data mining in telemedicine," Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications, Jan. 2020.

A. Nikam, S. Bhandari, A. Mhaske, and S. Mantri, "Cardiovascular Disease Prediction Using Machine Learning Models," in 2020 IEEE Pune Section International Conference (PuneCon), Pune, India, Dec. 2020, pp. 22–27.

H. Kamel and J. S. Healey, "Cardioembolic Stroke," Circulation Research, vol. 120, no. 3, pp. 514–526, Feb. 2017.

N. N. Islam Prova, "Healthcare Fraud Detection Using Machine Learning," in 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, Aug. 2024, pp. 1119–1123.

K. G. Dinesh, K. Arumugaraj, K. D. Santhosh, and V. Mareeswari, "Prediction of Cardiovascular Disease Using Machine Learning Algorithms," in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, Mar. 2018, pp. 1–7.

K. A. Hicks et al., "2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials," Circulation, vol. 137, no. 9, pp. 961–972, Feb. 2018.

M. Juchartz, "Plan comparison of Hybrid Dynamic Conformal Arc Therapy (HDCAT) and 3-Dimensional Conformal Radiation Therapy (3DCRT) in palliative treatment of thoracic spine tumors," Culminating Experience Projects, Aug. 2024, Art. no. 453.

B. H. Huang, M. J. Duncan, P. A. Cistulli, N. Nassar, M. Hamer, and E. Stamatakis, "Sleep and physical activity in relation to all-cause, cardiovascular disease and cancer mortality risk," British Journal of Sports Medicine, vol. 56, no. 13, pp. 718–724, Jul. 2022.

K. Adelborg et al., "Migraine and risk of cardiovascular diseases: Danish population based matched cohort study," BMJ, Jan. 2018, Art. no. k96.

M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, "Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison," Computers in Biology and Medicine, vol. 136, Sep. 2021, Art. no. 104672.

R. A. Bernstein et al., "Effect of Long-term Continuous Cardiac Monitoring vs Usual Care on Detection of Atrial Fibrillation in Patients With Stroke Attributed to Large- or Small-Vessel Disease: The STROKE-AF Randomized Clinical Trial," JAMA, vol. 325, no. 21, Jun. 2021, Art. no. 2169.

J. M. Castellano et al., "Polypill Strategy in Secondary Cardiovascular Prevention," New England Journal of Medicine, vol. 387, no. 11, pp. 967–977, Sep. 2022.

I. Hussain and S. J. Park, "Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management," IEEE Access, vol. 9, pp. 123146–123164, 2021.

A. Khosla, Y. Cao, C. C. Y. Lin, H. K. Chiu, J. Hu, and H. Lee, "An integrated machine learning approach to stroke prediction," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Washington DC USA, Jul. 2010, pp. 183–192.

C. S. Dangare and S. S. Apte, "Improved study of heart disease prediction system using data mining classification techniques," International Journal of Computer Applications, vol. 47, no. 10, pp. 44–48.

A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning," BMC Medical Informatics and Decision Making, vol. 19, no. 1, Dec. 2019, Art. no. 211.

Purushottam, K. Saxena, and R. Sharma, "Efficient heart disease prediction system using decision tree," in International Conference on Computing, Communication & Automation, Greater Noida, India, May 2015, pp. 72–77.

K. Battula, R. Durgadinesh, K. Suryapratap, and G. Vinaykumar, "Use of Machine Learning Techniques in the Prediction of Heart Disease," in 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, Mauritius, Oct. 2021, pp. 1–5.

D. Zhao, J. Liu, M. Wang, X. Zhang, and M. Zhou, "Epidemiology of cardiovascular disease in China: current features and implications," Nature Reviews Cardiology, vol. 16, no. 4, pp. 203–212, Apr. 2019.

Md. R. Ahmed, S. M. Hasan Mahmud, M. A. Hossin, H. Jahan, and S. R. Haider Noori, "A Cloud Based Four-Tier Architecture for Early Detection of Heart Disease with Machine Learning Algorithms," in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, Dec. 2018, pp. 1951–1955.

M. Y. Henein, S. Vancheri, G. Longo, and F. Vancheri, "The Role of Inflammation in Cardiovascular Disease," International Journal of Molecular Sciences, vol. 23, no. 21, Oct. 2022, Art. no. 12906.

E. L. Harshfield et al., "Association Between Depressive Symptoms and Incident Cardiovascular Diseases," JAMA, vol. 324, no. 23, Dec. 2020, Art. no. 2396.

R. Eikelboom, R. Sanjanwala, M.-L. Le, M. H. Yamashita, and R. C. Arora, "Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-Analysis," The Annals of Thoracic Surgery, vol. 111, no. 2, pp. 544–554, Feb. 2021.

D. Smajlovic, "Strokes in young adults: epidemiology and prevention," Vascular Health and Risk Management, Feb. 2015, Art. no. 157.

B. P. Doppala, D. Bhattacharyya, M. Janarthanan, and N. Baik, "A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques," Journal of Healthcare Engineering, vol. 2022, pp. 1–13, Mar. 2022.

Md. H. R. Sobuz et al., "Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation," Heliyon, vol. 10, no. 6, Mar. 2024, Art. no. e27793.

Downloads

How to Cite

[1]
Tasnim, A.F., Rahman, R., Prabha, M., Hossain, M.A., Nilima, S.I., Mahmud, M.A.A. and Erdei, T.I. 2205. Explainable Machine Learning Algorithms to Predict Cardiovascular Strokes. Engineering, Technology & Applied Science Research. 15, 1 (Feb. 2205), 20131–20137. DOI:https://doi.org/10.48084/etasr.9152.

Metrics

Abstract Views: 118
PDF Downloads: 13

Metrics Information