Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology

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Anita Petreska
Daniela Slavkovska


BACKGROUND: Recent years have witnessed the widespread adoption of machine learning (ML) and deep learning techniques in various health-care applications. Artificial intelligence and ML algorithms using big medical data make it possible to predict diseases and enable the development of personalized treatments for patients. Heart diseases are one of the most common chronic diseases affecting human health, and early detection can reduce the mortality rate.

AIM: We aimed to review different types of ML techniques and their applications in heart disease risk detection.

METHODS: For different cardiovascular diseases, the choice of algorithms should be tailored based on their accuracy and efficiency

RESULTS: The research presented highlights the critical global issue of heart disease and its impact on public health. The urgency to address this global problem is emphasized, as heart disease has become a significant factor in the increasing mortality rate worldwide. The introduction of ML in the prognosis of heart disease is a significant step toward realizing predictive, preventive, and personalized health care and reducing health-care costs. In this study, a comparative evaluation of ML models was made: Logistic regression, decision tree, random forest, and support vector machine. The quality of the data, as well as the choice of an appropriate algorithm, is key factors in the assessment of heart diseases.

CONCLUSION: Despite the impressive performance of ML, there are doubts about its robustness in traditional health-care systems due to many security and privacy issues.


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Petreska A, Slavkovska D. Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology. SEE J Cardiol [Internet]. 2024 Mar. 10 [cited 2024 May 20];5:17-25. Available from:
General Cardiology


Dissanayake K, Johar MG. Comparative study on heart disease prediction using feature selection techniques on classification algorithms. Appl Comput Intell Soft Comput. 2021;2021:5581806. DOI:

Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int J Comput Appl. 2011;17(8):43-48. DOI:

Slavkovska D, Ristevski B, Petreska A. Comparative Analysis of ML Algorithms for Breast Cancer Detection. In: 13th International Conference on Applied Internet and Information Technologies AIIT2023. Bitola: Rebublic North Macedonia; 2023. p. 151-61.

Petreska A, Ristevski B, Slavkovska D, Nikolovski S, Spirov P, Rendevski N, et al. Machine Learning Algorithms for Heart Disease Prognosis Using IoMT Devices. In: 13th International Conference on Applied Internet and Information Technologies AIIT2023. Bitola: Rebublic North Macedonia; 2023. p. 141-50.

Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 2019;7:81542-54. DOI:

Amin MS, Chiam YK, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 2019;36(1):82-93. DOI:

Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Comput Sci. 2020;1(6):1-6. DOI:

Ali MM, Paul BK, Ahmed K, Bui FM, Quinn JM, Moni MA. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput Biol Med. 2021;136:104672. PMid:34315030 DOI:

Ramesh TR, Lilhore UK, Poongodi M, Simaiya S, Kaur A, Hamdi M. Predictive analysis of heart diseases with machine learning approaches. Malays J Comput Sci. 2022;2022(1):132- 48. DOI:

Ayon SI, Islam MM, Hossain MR. Coronary artery heart disease prediction: A comparative study of computational intelligence techniques. IETE J Res. 2022;68(4):2488-507. DOI:

Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS. Heart Disease Prediction Using Hybrid Machine Learning Model. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT). Piscataway: IEEE; 2021. DOI:

Mienye ID, Sun Y, Wang Z. An improved ensemble learning approach for the prediction of heart disease risk. Inform Med Unlocked. 2020;20:100402. DOI:

Gupta A, Kumar R, Arora HS, Raman B. MIFH: A machine intelligence framework for heart disease diagnosis. IEEE Access. 2019;8:14659-74. DOI:

Latha CB, Jeeva SC. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform Med Unlocked. 2019;16:100203. DOI:

Ghosh P, Azam S, Jonkman M, Karim A, Shamrat FM, Ignatious E, et al. Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access. 2021;9:19304-26. DOI:

Available from: heart-disease-prediction-using-logistic-regression [last accessed on 2023 Jan 15].

Mahalakshmi K, Sujatha P. The Role of Exploratory Data cessing in the Machine Learning Predictive Model for Heart Disease. In: 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). Piscataway: IEEE; 2023. DOI:

Arumugam K, Naved M, Shinde PP, Leiva-Chauca O, Huaman- Osorio A, Gonzales-Yanac T. Multiple disease prediction using machine learning algorithms. Mater Today Proc. 2023;80:3682- 5. DOI:

Chandrasekhar N, Peddakrishna S. Enhancing heart disease prediction accuracy through machine learning techniques and optimization. Processes. 2023;11(4):1210. DOI:

Du Z, Yang Y, Zheng J, Li Q, Lin D, Li Y, et al. Accurate prediction of coronary heart disease for patients with hypertension from electronic health records with big data and machine-learning methods: Model development and performance evaluation. JMIR Med Inform. 2020;8(7):e17257. PMid:32628616 DOI:

Nagavelli U, Samanta D, Chakraborty P. Machine learning technology-based heart disease detection models. J Healthc Eng. 2022;2022:7351061. PMid:35265303 DOI:

Saboor A, Usman M, Ali S, Samad A, Abrar MF, Ullah N. A method for improving prediction of human heart disease using machine learning algorithms. Mob Inform Syst. 2022;2022(15):1410169. DOI:

Mahoto NA, Shaikh A, Sulaiman A, Al Reshan MS, Rajab A, Rajab K. A machine learning based data modeling for medical diagnosis. Biomed Signal Process Control. 2023;81:104481. DOI:

Ozcan M, Peker S. A classification and regression tree algorithm for heart disease modeling and prediction. Healthc Anal. 2023;3:100130. DOI:

Al Ahdal A, Rakhra M, Rajendran RR, Arslan F, Khder MA, Patel B, et al. Monitoring cardiovascular problems in heart patients using machine learning. J Healthc Eng. 2023;2023:9738123. PMid:36818386 DOI:

Kadhim MA, Radhi AM. Heart disease classification using optimized Machine learning algorithms. Iraqi J Comput Sci Math. 2023;4(2):31-42. DOI:

Dalal S, Goel P, Onyema EM, Alharbi A, Mahmoud A, Algarni MA, et al. Application of machine learning for cardiovascular disease risk prediction. Comput Intell Neurosci. 2023;2023:9418666. DOI:

Shukur BS, Mijwil MM. Involving machine learning techniques in heart disease diagnosis: A performance analysis. Int J Electr Comput Eng. 2023;13(2):2177-85. DOI: