A Scoping Review of Machine Learning Techniques and Their Utilisation in Predicting Heart Diseases
Main Article Content
Abstract
Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms, which leads to severe concerns such as heart failure and stroke and kidney failure. In this regard, the authors highlight an amount of literature considered the most practical in utilizing machine learning techniques in predicting heart disease. Twenty articles were chosen out of fifty articles gathered and summarised in a table form. The main goal is to make this article a reference that can be utilized in the future to assist healthcare workers in studying these techniques with ease and saving time and effort on them. This article has concluded that machine learning techniques have a significant and influential role in analyzing disease data, predicting heart disease, and assisting decision-making. In addition, these techniques can analyze data that reaches millions of cohorts.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
licenseTermsHow to Cite
Publication Dates
References
Hoffman, D. J.; Reynolds, R. M.; Hardy, D. B. Developmental origins of health and disease: current knowledge and potential mechanisms. Nutr Rev. 2017, 75 (12), 951-970.
Mijwil, M. M.; Al-Mistarehi, AH; Mutur, D. S. The Practices of Artificial Intelligence Techniques and Their Worth in the Confrontation of COVID-19 Pandemic: A Literature Review. Mob Forensics J. 2022, 4(1), 11-30.
Núñez-Gil, I. J.; Fernández-Ortiz, A.; Eid, C. M.; Huang, J.; Romero, R.; Becerra-Muñoz, V. M. Underlying heart diseases and acute COVID-19 outcomes. Cardiol J. 2020, 28 (2), 202-214.
Taylor, K.; Elhakeem, A.; Nader, J. L. T.; Yang, T. C.; Isaevska, E.; Richiardi, L.; Vrijkotte, T.; Moira, A. P.; Murray, D. M.; Finn, D.; Mason, D.; Wright, J.; Oddie, S.; Roeleveld, N.; Harris, J. R.; Andersen, A. N.; Caputo, M.; Lawlor, D. A. Effect of Maternal Prepregnancy/Early‐Pregnancy Body Mass Index and Pregnancy Smoking and Alcohol on Congenital Heart Diseases: A Parental Negative Control Study. J Am Heart Assoc. 2021,10(11),1-78.
Mijwil, M. M.; Al-Mistarehi, AH.; Aggarwal, K. The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review. Asian J Appl Sci. 2021, 9 (5), 343-352.
Aggarwal, K.; Mijwil, M. M.; Al-Mistarehi, AH.; Alomari, S.; Gök, M;, Alaabdin, A. M.; Abdulrhman, S. H. Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning. Iraqi J Comput Sci Math. 2022, 3(1), 115-123.
Ostheimer, J.; Chowdhury, S.; Sarfraz, I. An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles. Technol Soc. 2021, 66, 101647.
Guzman, A. L.; Lewis, S. C. Artificial intelligence and communication: A Human–Machine Communication research agenda. New Media Soc. 2019, 22(1), 70-86.
Mijwil, M. M.; Salem, I. E; Abttan, R. A. Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets. Asian J Comput Inf Sys. 2021, 9(5), 29-34.
Matsuzaka, Y.; Uesawa, Y. A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning. Curr Issues Mol Biol. 2020, 42(1), 455-472.
Rasheed, J.; Hameed, A. A.; Djeddi, C.; Jamil, A.; Al-Turjman, F. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci. 2021,13, 103-117.
Mijwil, M. M. Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans. Iraqi J Sci. 2021, 62 (6), 2099-2109.
Farhan, B. I.; Jasim, A. D. A Survey of Intrusion Detection Using Deep Learning in Internet of Things. Iraqi J Comput Sci Math, 2022, 3 (1), 83-93.
Rudin, C; Ustun, B. Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. INFORMS J Appl Anal. 2018, 48 (5), 449-466.
Johnson, K. W.; Soto, J. T.; Glicksberg, B. S.; Shameer, K.; Miotto, R.; MPhil, M. A.; Ashley, E.; Dudley J. T. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018, 71(23), 2668-2679.
Rimal, Y. Naïve Bayes Machine Learning Classification with R Programming: a Case Study of Binary Data Sets. Int J Orange technol. 2019, 1 (2), 27-34.
Albashrawi, M. Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015. J Data Sci. 2016, 14(3), 553-569.
Djerioui, M.; Brik, Y.; Ladjal, M.; Attallah, B. Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction. Ing des Syst d'Information. 2019, 24 (6), 591-595.
Cui, S.; Li, K.; Ang, L.; Liu, J.; Cui, L.; Song, X.; Lv, S.; Mahmud, E. Plasma Phospholipids and Sphingolipids Identify Stent Restenosis After Percutaneous Coronary Intervention. JACC Cardiovasc Interv. 2017, 10 (13), 1307-1316.
Laitinen, E. K; Laitinen, T. Bankruptcy prediction: Application of the Taylor's expansion in logistic regression. Int Rev Financ Anal. 2020, 9 (4), 327-349,
Pérez, M. D. F; Pérez, J. C. R. La regresión logística: una herramienta versátil. Nephrology. 2000, 20, (6), 477-565.
Rodrigues, É. O. Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier. Pattern Recognit Lett. 2018, 110, 66-71.
Abedi, R.; Bonyad, A. E.; Moridani, A. Y.; Shahbahrami, A. Evaluation of IRS and Landsat 8 OLI imagery data for estimation forest attributes using k nearest neighbour non-parametric method. Int J Image Data Fusion. 2018, 9(4), 287-301.
Belgiu, M; Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens. 2016, 114, 24-31,.
Wang, Y.; Xiu, C.; Zhang, X.; Yang, D. WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest. Sensors, 2018, 18 (9), 1-23.
Ghosal, S.; Sengupta, S.; Majumder, M.; Sinha B. Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020). Diabetes Metab Syndr. 2020, 14 (4), 311-315.
Zhang, D.; Zhao, Y.; Du, M.; A Novel Supervised Feature Extraction Algorithm: Enhanced Within-class Linear Discriminant Analysis. Int J Comput Sci Eng. 2016, 13(1),13-23.
Mouloodi, S.; Rahmanpanah, H.; Gohari, S.; Burvill, C.; Tse, K. M.; Davies H. M. S. What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research. J Mech Behav Biomed Mater. 2021, 123, 104728.
Badbe, V. C.; Londhe, V.; Shirole, G. Analysis of Heart Disease By LVQ in Neural Network. Int j recent innov trends comput commun. 2016, 4(4), 603-604.
Putri, N. K.; Rustam, Z.; Sarwinda, D. Learning Vector Quantization for Diabetes Data Classification with Chi-Square Feature Selection. IOP Conf Ser Mater Sci Eng. 2019, 546, 1-8.
Bharti, R.; Khamparia, A.; Shabaz, M.; Dhiman, G.; Pande, S.; Singh, P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Comput Intell Neurosci. 2021, 8387680, 1-11.
Weng, S. F.; Reps, J.; Kai, J.; Garibaldi, J. M.; Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE, 2017, 12(4),1-14.
Budzianowski, J.; Hiczkiewicz, J.; Burchardt, P.; Pieszko, K.; Rzeźniczak, J.; Budzianowski, P.; Korybalska, K. Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms. Heart Vessels. 2018, 34, 352-359.
Nanayakkara, S.; Fogarty, S.; Tremeer, M.; Ross, K.; Richards, B.; Bergmeir, C.; Xu, S.; Stub, D.; Smith, K.; Tacey, M.; Liew, D.; Pilcher, D.; Kaye, D. M. Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study. PLoS Medicine. 2018, 15 (11), 1-16.
Yıldırım, Ö.; Pławiak, P.; Tan, R.; Acharya, U. R. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. 2018, 102, 411-420.
Ul Haq, A.; Li, J. P.; Memon, M. H.; Nazir, S.; Sun R. A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms. Mob Inf Syst. 2018, 2018, 1-22.
Tesche, C.; De Cecco, C. N.; Baumann, S.; Renker, M.; McLaurin, T. W.; Duguay, T. M.; Bayer, R. R.; Steinberg, D. H.; Grant, K. L.; Canstein, C.; Schwemmer, C.; Schoebinger, M.; Itu, L. M.; Rapaka, S.; Sharma, P.; Schoepf, U. J. Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology, 2018, 288(1),64-72.
Attia, Z. I.; Noseworthy, P. A.; Lopez-Jimenez, F.; Asirvatham, S. J.; Deshmukh, A. J.; Gersh B. J. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019, 394(10201), 861-867.
Dinh, A.; Miertschin, S.; Young, A.; Mohanty, S. D. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Inform Decis Mak. 2019, 19, 1-15.
Oikonomou, E. K.; Williams, M. C.; Kotanidis, C. P.; Desai, M. Y.; Marwan, M.; Antonopoulos, A. S. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J. 2019, 40 (43), 3529–3543.
Hill, N. R.; Ayoubkhani, D.; McEwan, P.; Sugrue, D. M.; Farooqui, U.; Lister, S.; Lumley, M.; Bakhai, A.; Cohen, A. T.; O’Neill, M.; Clifton, D.; Gordon, J. Predicting atrial fibrillation in primary care using machine learning. PLoS ONE, 2019, 14(11), 1-13.
Tiwari, P; Colborn, K. L.; Smith, D. E.; Xing, F.; Ghosh, D.; Rosenberg, M. A. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw Open. 2020, 3(1),1-12.
Alhusseini, M. I.; Abuzaid, F.; Rogers, A. J.; Zaman, J. A. B.; Baykaner, T.; Clopton, P.; et al. Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation. Circ Arrhythm Electrophysiol. 2020, 13(8), e008160.
Loring, Z.; Mehrotra, S.; Piccini, J. P.; Camm, J.; Carlson, D.; Fonarow, G. C.; Fox, K. A. A.; Peterson, E. D.; Pieper, K.; Kakkar, A. K. Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries. EP Europace. 2020, 22(11), 1635–1644.
Ward, A.; Sarraju, A.; Chung, S.; Li, J.; Harrington, R.; Heidenreich, P.; Palaniappan, L.; Scheinker, D.; Rodriguez, F. Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ Digit Med. 2020, 3(125), 1-7.
Krittanawong, C.; Virk, H. U.; Bangalore, S.; Wang, Z.; Johnson, K. W.; Pinotti R.; Zhang, H.; Kaplin, S.; Narasimhan, B.; Kitai, T.; Baber, U.; Halperin, J. L.; Tang, W. H. W. Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep. 2020, 10, 16057, 1-11.
Vinter, N.; Frederiksen, A. S.; Albertsen, A. E.; Lip, G. Y. H.; Fenger-Grøn, M.; Trinquart, L; Frost, L.; Møller, D. S. Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?. OpenHeart. 2020, 7, 1-7.
Wang, S.; Li, J.; Sun, L.; Cai, J.; Wang, S.; Zeng, L.; Sun, S. Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction. BMC Medical Inform Decis Mak. 2021, 21(301), 1-14.
Lip, G. Y. H.; Genaidy, A.; Tran, G.; Marroquin, P. Estes C. Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach. Eur J Intern Med. 2021, 91, 53-58.
Wang, Q.; Li, B.; Chen, K.; Yu, F.; Su, H.; Hu, K.; et al. Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure. ESC Heart Fail. 2021, 8(6), 5363–5371.
Khurshid, S.; Friedman, S.; Reeder, C.; Achille, P. D.; Diamant, N.; Singh, P.; Harrington, L. X.; Wang, X.; Al-Alusi, M. A.; Sarma, G.; Foulkes, A. S.; Ellinor, P. T.; Anderson, C. D.; Ho, J. E.; Philippakis, A. A.; Batra, P.; Lubitz, S. A. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2021, 145(2), 122-133.