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International Journal of Preclinical and Clinical Research

Article

Heart Disease Prediction using Artificial Intelligence and Machine Learning
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International Journal of Preclinical and Clinical Research

DOI: 10.51131/IJPCCR/v4i2.23.11

Year: 2023, Volume: 4, Issue: 2, Pages: 32-37

Review Article

Heart Disease Prediction using Artificial Intelligence and Machine Learning

N R Chinmayi​ ✉ 1

 
1 MedRite Division, Ascentia Innovations, Davangere, Karnataka, 577004, India

Corresponding author ✉: [email protected]


Received Date:25 July 2023, Accepted Date:05 August 2023, Published Date:14 August 2023

Creative Commons License

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

Abstract

Heart-related medical conditions, often known as cardiovascular diseases (CVDs), have emerged as the main cause for death worldwide over the past several decades and are now recognized as the most serious medical conditions in both India and the rest in the entire world. This study focuses on the development of a machine learning-based artificial intelligence system for the identification of heart disease. Diagnose, risk stratification, and management are basically a few of the critical thinking-intensive aspects of medical treatment that have been mechanized because of the development of artificial intelligence (AI) and data science, which has reduced the strain on doctors and decreased the possibility of human error. Using the primary clinical outcomes of each CHD (Congenital heart defects) and the right computational algorithms, risk stratification as well as the prediction of treatment results are feasible. Cardiovascular diseases (CVDs) have a significant economic burden for healthcare systems all over the world and are associated with high morbidity and mortality. In the United States, adults had a prevalence of 7.2% for coronary artery disease, which is one disease category included under the CVDs umbrella. In the years 2016–2017, this disease entity damaged the United States economy 360 billion US dollars. The domain of cardiovascular medicine has expanded in new directions because of the development of artificial intelligence (AI) as well as machine learning over the past two decades. It will be highly beneficial to the treatment of this disease if we are able to predict cardiac arrest in the early stages. A quick and effective detection method must be developed in order to decrease the enormous number of deaths caused by cardiovascular diseases. In order to increase the model's ability for predicting heart disease in every individual, an effective approach was utilized to control how it can be used.

 

Keywords: Artificial intelligence, Pediatric cardiac surgeries, Heart disease detection system, Cardiovascular diseases, Coronary artery disease, Congenital heart defects

References

  1. Chang V, Bhavani VR, Xu AQ, Hossain MA. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthcare Analytics. 2022;2(100016). Available from: https://doi.org/10.1016/j.health.2022.100016
  2. Quesada JA, Lopez‐pineda A, Gil‐guillén VF, Durazo‐arvizu R, Orozco‐beltrán D, López-Domenech A, et al. Machine learning to predict cardiovascular risk. International Journal of Clinical Practice. 2019;73(10):e13389. Available from: https://doi.org/10.1111/ijcp.13389
  3. Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Informatics and Decision Making. 2019;19(1):211–215. Available from: https://doi.org/10.1186/s12911-019-0918-5
  4. Nadakinamani RG, Reyana A, Kautish S, Vibith AS, Gupta Y, Abdelwahab SF, et al. Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques. Computational Intelligence and Neuroscience. 2022;2022(2973324):1–13. Available from: https://doi.org/10.1155/2022/2973324
  6. Prabhakaran D, Jeemon P, Roy A. Cardiovascular Diseases in India. Circulation. 2016;133(16):1605–1620.
  7. Kumar AS, Sinha N. Cardiovascular disease in India: A 360 degree overview. Cardiovascular disease in India: A 360-degree overview. 2020;76(1):1–3. Available from: https://doi.org/10.1016%2Fj.mjafi.2019.12.005
  9. Gupta R, Joshi P, Mohan V, Reddy KS, Yusuf S. Epidemiology and causation of coronary heart disease and stroke in India. Heart. 2008;94(1):16–26. Available from: https://doi.org/10.1136/hrt.2007.132951
  10. Huffman MD, Prabhakaran D, Osmond C, Fall CHD, Tandon N, Lakshmy R, et al. Incidence of Cardiovascular Risk Factors in an Indian Urban Cohort. Europe PMC Funders Group Author Manuscript. Journal of the American College of Cardiology. 2011;57(17):1765–1774. Available from: https://doi.org/10.1016%2Fj.jacc.2010.09.083
  11. Snousy MBA, El-Deeb HM, Badran K, Khlil IAA. Suite of decision tree-based classification algorithms on cancer gene expression data. Egyptian Informatics Journal. 2011;12(2):73–82. Available from: https://doi.org/10.1016/j.eij.2011.04.003
  13. Eynde JVD, Kutty S, Danford DA, Manlhiot C. Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data. Current Opinion in Cardiology. 2022;37(1):130–136. Available from: https://doi.org/10.1097/hco.0000000000000927
  14. Basu K, Sinha R, Ong A, Basu T. Artificial intelligence: How is it changing medical sciences and its future? Indian Journal of Dermatology. 2020;65(5):365–370. Available from: https://doi.org/10.4103/ijd.ijd_421_20
  15. Barsh YIA, Duhair MK, Ismail HJ, Abu-Nasser BS, Abu-Naser SS. MPG Prediction Using Artificial Neural Network. International Journal of Academic Information Systems Research (IJAISR). 2020;4(11):7–16. Available from: http://ijeais.org/wp-content/uploads/2020/11/IJAISR201102.pdf
  16. Alajrami E. Blood Donation Prediction using Artificial Neural Network. International Journal of Academic Engineering Research (IJAER). 2019;3(10):1–7. Available from: http://dstore.alazhar.edu.ps/xmlui/bitstream/handle/123456789/489/IJAER191001alaa.pdf?sequence=1&isAllowed=y
  17. Seh AH, Chaurasia PK. A Review on Heart Disease Prediction Using Machine Learning Technique. International journal of Management, IT and Engineering. 2019;9(4):208–224.
  18. Gaffar S, Gearhart AS, Chang AC. The Next Frontier in Pediatric Cardiology. Pediatric Clinics of North America. 2020;67(5):995–1009. Available from: https://doi.org/10.1016/j.pcl.2020.06.010
  19. Bertsimas D, Zhuo D, Dunn J, Levine J, Zuccarelli E, Smyrnakis N. Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach. World Journal for Pediatric and Congenital Heart Surgery. 2021;12(4):453–460. Available from: https://doi.org/10.1177/21501351211007106
  20. Samad MD, Wehner GJ, Arbabshirani MR, Jing LMR, Powell AJ, Geva TAJ, et al. Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning. European Heart Journal - Cardiovascular Imaging. 2018;19(7):730–738. Available from: https://doi.org/10.1093/ehjci/jey003
  21. Shi H, Yang D, Tang K, Hu C, Li L, Zhang L, et al. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clinical Nutrition. 2022;41(1):202–210. Available from: https://doi.org/10.1016/j.clnu.2021.11.006
  22. Dias RD, Shah JA, Zenati MA. Artificial intelligence in cardiothoracic surgery. Minerva Cardioangiologica. 2020;68(5):532–538. Available from: https://doi.org/10.23736%2FS0026-4725.20.05235-4
  23. Wilhelm D, Ostler D, Müller-Stich B, Lamadé W, Stier A, Feußner H. Artificial intelligence in general and visceral surgery. Der Chirurg. 2020;91:181–189. Available from: https://doi.org/10.1007/s00104-019-01090-w
  24. Arafati A, Hu P, Finn JP, Rickers C, Cheng AL, Jafarkhani H, et al. Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need. Cardiovascular Diagnosis and Therapy. 2019;9(S2):S310–S325. Available from: https://doi.org/10.21037/cdt.2019.06.09
  25. Xu X, Qiu H, Jia Q, Dong Y, Yao Z, Xie W. AI-CHD: An AI-Based Framework for Cost-Effective Surgical Telementoring of Congenital Heart Disease. Communications of the ACM. 2021;64(12):66–74. Available from: https://doi.org/10.1145/3450409
  26. Mahayni AA, Attia ZI, Medina-Inojosa JR, Elsisy MFA, Noseworthy PA, Lopez-Jimenez FA, et al. Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery. Mayo Clinic Proceedings. 2021;96(12):3062–3070. Available from: https://doi.org/10.1016/j.mayocp.2021.06.024
  27. Newburger JW, Takahashi M, Burns JC, Disease K. Kawasaki Disease. Journal of the American College of Cardiology. 2016;67(14):1738–1749. Available from: https://doi.org/10.1016/j.jacc.2015.12.073
  28. Ghosh P, Katkar GD, Shimizu C, Kim J, Khandelwal S, Tremoulet AH, et al. An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease. Nature Communications. 2022;13(1):2687. Available from: https://doi.org/10.1038/s41467-022-30357-w
  29. Bayers S, Shulman ST, Paller AS. Kawasaki disease: part I. Diagnosis, clinical features, and pathogenesis. Journal of the American Academy of Dermatology. 2013;69(4):e1–e11. Available from: https://doi.org/10.1016/j.jaad.2013.07.002
  31. Benovoy M, Dionne A, Mccrindle BW, Manlhiot CW, Ibrahim R, Dahdah N. Deep Learning-Based Approach to Automatically Assess Coronary Distensibility Following Kawasaki Disease. Pediatric Cardiology. 2022;43(4):807–815. Available from: https://doi.org/10.1007/s00246-021-02790-z
  32. Martins JFBS, Nascimento ER, Nascimento BR, Sable CA, Beaton AZ, Ribeiro AL, et al. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. Journal of the American Medical Informatics Association. 2021;28(9):1834–1842. Available from: https://doi.org/10.1093/jamia/ocab061
  33. Asmare MH, Woldehanna F, Janssens L, Vanrumste B. Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020;p. 168–171.
  34. Malik P, Pathania M, Rathaur VK. Overview of Artificial Intelligence in Medicine. Journal of Family Medicine and Primary Care. 2019;8:2328–2331. Available from: https://doi.org/10.4103%2Fjfmpc.jfmpc_440_19
  35. Basu K, Sinha R, Ong A, Basu T. Artificial intelligence: How is it changing medical sciences and its future? Indian Journal of Dermatology. 2020;65(5):365–370. Available from: https://doi.org/10.4103%2Fijd.IJD_421_20
  36. Helman SM, Herrup EA, Christopher AB, Al-Zaiti SS. The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. Cardiology in the Young. 2021;31(11):1770–1780. Available from: https://doi.org/10.1017/s1047951121004212
  37. Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Computers in Biology and Medicine. 2020;122(103801). Available from: https://doi.org/10.1016/j.compbiomed.2020.103801
  38. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. 2020;2020:295–336. Available from: https://doi.org/10.1016%2FB978-0-12-818438-7.00012-5
  39. Diamond GA, Forrester JS. Analysis of Probability as an Aid in the Clinical Diagnosis of Coronary-Artery Disease. New England Journal of Medicine. 1979;300(24):1350–1358. Available from: https://doi.org/10.1056/nejm197906143002402
  40. Foldyna B, Udelson JE, Karády J, Banerji D, Lu MT, Mayrhofer TMT, et al. Pretest probability for patients with suspected obstructive coronary artery disease: re-evaluating Diamond–Forrester for the contemporary era and clinical implications: insights from the PROMISE trial. European Heart Journal - Cardiovascular Imaging. 2019;20(5):574–581. Available from: https://doi.org/10.1093/ehjci/jey182
  41. Genders TSS, Steyerberg EW, Alkadhi HEW, Leschka S, Desbiolles L, Nieman K, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. European Heart Journal. 2011;32(11):1316–1330. Available from: https://doi.org/10.1093/eurheartj/ehr014
  42. Genders TSS, Steyerberg EW, Hunink MGM, Nieman KG, Galema TW, Mollet NR, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344(jun12 1):e3485. Available from: https://doi.org/10.1136/bmj.e3485
  44. Li D, Xiong G, Zeng H, Zhou Q, Jiang J, Guo X. Machine learning-aided risk stratification system for the prediction of coronary artery disease. International Journal of Cardiology. 2021;326:30–34. Available from: https://doi.org/10.1016/j.ijcard.2020.09.070
  45. Velusamy D, Ramasamy K. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Computer Methods and Programs in Biomedicine. 2021;198(105770). Available from: https://doi.org/10.1016/j.cmpb.2020.105770
  46. Muhammad LJ, Al-Shourbaji I, AAH, IAM, Ahmad A, Jibrin MB. Machine Learning Predictive Models for Coronary Artery Disease. SN Computer Science. 2021;2(5):350. Available from: https://doi.org/10.1007%2Fs42979-021-00731-4
  47. Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. European Heart Journal. 2020;41(46):4400–4411. Available from: https://doi.org/10.1093/eurheartj/ehaa640
  48. Budoff MJ, Mayrhofer T, Ferencik M, Bittner D, Lee KL, Lu MT, et al. Prognostic Value of Coronary Artery Calcium in the PROMISE Study (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) Circulation. 2017;136(21):1993–2005. Available from: https://doi.org/10.1161/circulationaha.117.030578

Copyright

© 2023 Chinmayi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Basaveshwara Medical College & Hospital, Chitradurga, Karnataka

  • 14 August 2023

Year: 2023, Volume: 4, Issue: 2


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