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Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis

Published: 05 July 2019 Publication History

Abstract

The field of medical analysis is often referred to be a valuable source of rich information. Coronary Heart Disease (CHD) is one of the major causes of death all around the world therefore early detection of CHD can help reduce these rates. The challenge lies in the complexity of the data and correlations when it comes to prediction using conventional techniques. The aim of this research is to use the historical medical data to predict CHD using Machine Learning (ML) technology. The scope of this research is limited to using three supervised learning techniques namely Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT), to discover correlations in CHD data that might help improving the prediction rate. Using the South African Heart Disease dataset of 462 instances, intelligent models are derived by the considered ML techniques using 10-fold cross validation. Empirical results using different performance evaluation measures report that probabilistic models derived by NB are promising in detecting CHD.

References

[1]
Abdelhamid N., Thabtah F., (2014) Associative Classification Approaches: Review and Comparison. Journal of Information and Knowledge Management (JIKM). Vol. 13, No. 3 (2014) 1450027.
[2]
Abdelhamid N., Ayesh A., Thabtah F. (2012) An Experimental Study of Three Different Rule Ranking Formulas in Associative Classification Mining. Proceedings of the 7th IEEE International Conference for Internet Technology and Secured Transactions (ICITST-2012), pp. (795--800), UK.
[3]
Apte, C. S. (2012). Improve study of Heart Disease prediction system using Data Mining Classification techniques. International journal of computer application, 47(10), 44--48.
[4]
Hadi W., Thabtah F., Mousa S., ALHawari S., Kanaan G., Ababnih J. (2008). A Comprehensive Comparative Study using Vector Space Model with K-Nearest Neighbor on Text Categorization Data. Journal of Applied Sciences, volume 2:1-pp. 12--24. Science Alert.
[5]
Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. (2009) The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
[6]
Hassan, S. A., & Khan, T. (2017). A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers. International Journal of Advanced Computer Science and Applications, 8(12), 124--131.
[7]
Hazra, A., Mandal, S. K., Gupta, A., & Mukherjee, A. (2017). Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review. Advances in Computational Sciences and Technology, 10(7), 2137--2159. Retrieved from https://www.researchgate.net/publication/319393368_Heart_Disease_Diagnosis_and_Prediction_Using_Machine_Learning_and_Data_Mining_Techniques_A_Review
[8]
Jenzi, P. D. (2013). A Reliable Classifier Model Using Data Mining Approach for Heart Disease Prediction. International Journal of Advanced Research in Computer Science and Software Engineering, 3.
[9]
John, G. H., & Langley, P. (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 338--345). San Mateo: Morgan Kaufmann Publishers.
[10]
Kalhori, S. R., & Zeng, X.-J. (2013). Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course. Journal of Intelligent Learning Systems and Applications, 5(3), 184--193.
[11]
Karthiga, A. S., Mary, M. S., & M.Yogasini. (2017). Early Prediction of Heart Disease Using Decision Tree Algorithm. International Journal of Advanced Research in Basic Engineering Sciences and Technology, 3(3).
[12]
Kierkegaard, P. (2011). Electronic health record: Wiring Europe's healthcare. Computer Law & Security Review, 27(5), 503--515. Retrieved from https://www.sciencedirect.com/science/article/pii/S0267364911001257?via%3Dihub
[13]
King, M. A. (2018). Dementia could be detected via routinely collected data, new research shows. Retrieved from University of Plymouth Website: https://www.plymouth.ac.uk/news/dementia-could-be-detected-via-routinely-collected-data-new-research-shows
[14]
Manimekalai. K. (2016). Prediction of Heart Diseases using Data Mining Techniques. International Journal of Innovative Research in Computer and Communication Engineering, 4(2), 2161--2168. Retrieved from http://www.ijircce.com/upload/2016/february/73_27_Prediction.pdf
[15]
Mohammed R., Thabtah F., McCluskey L., (2013) Intelligent Rule based Phishing Websites Classification. Journal of Information Security (2), 1--17. ISSN 17518709. IET.
[16]
Platt, J. C., & Nitschke, R. v. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines.
[17]
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann Publishers.
[18]
Reddy, P. V., & Suryachandra, P. (2016). Comparison of machine learning algorithms for breast cancer. 2016 International Conference on Inventive Computation Technologies (ICICT), (pp. 1--6).
[19]
Sivakumar, S. (n.d.). Prediction of Coronary Heart Disease by learning from retrospective study. Retrieved from GitHub: http://srisai85.github.io/CHD/heart_attack.html
[20]
Southern Cross. (2018). Coronary heart disease - causes, symptoms, prevention. Retrieved from Southern Cross: https://www.southerncross.co.nz/group/medical-library/coronary-heart-disease-causes-symptoms-prevention
[21]
Thabtah F., Peebles D. (2019) A new machine learning model based on induction of rules for autism detection. Health Informatics Journal, 1460458218824711.
[22]
Thabtah F. (2018a) An Accessible and Efficient Autism Screening Method for Behavioural Data and Predictive Analyses. Health Informatics Journal. 19:1460458218796636. 2018.
[23]
Thabtah F. (2018b) Machine learning in autistic spectrum disorder behavioral research: A review and ways forward Informatics for Health and Social Care 43 (2), 1--20.
[24]
Thabtah F, Kamalov F., Rajab K (2018) A new computational intelligence approach to detect autistic features for autism screening. International Journal of Medical Infromatics, Volume 117, pp. 112--124.
[25]
Thabtah F. (2017) Autism Spectrum Disorder Tools: Machin Learning Adaptation and DSM-5 Fulfillment: An Investigative Study. Proceedings of the2017 International Conference on Medical and Health Informatics (ICMHI 2017), pp. 1--6. Taichung, Taiwan. ACM.
[26]
Thabtah F., Hammoud S (2013) MR-ARM: A MapReduce Association Rule Mining. Journal of Parallel Processing Letter, 23 (3) 1--22, 1350012. World Scientific.
[27]
World Health Organization. (2005). Preventing Chronic Diseases a vital investment. Switzerland: WHO Press.
[28]
Knowledge Extraction based on Evolutionary Learning. (2004-2018). South African Heart data set. Retrieved from KEEL (Knowledge Extraction based on Evolutionary Learning): https://sci2s.ugr.es/keel/dataset.php?cod=184.
[29]
Yanwei X, W. J. (2007). Combination data mining. Proceedings International Conference on Convergence Information Technology, (pp. 868--872).

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  1. Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis

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    cover image ACM Other conferences
    ICDLT '19: Proceedings of the 2019 3rd International Conference on Deep Learning Technologies
    July 2019
    106 pages
    ISBN:9781450371605
    DOI:10.1145/3342999
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Nanyang Technological University
    • Chongqing University of Posts and Telecommunications

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    New York, NY, United States

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    Published: 05 July 2019

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    Author Tags

    1. Coronary Heart Disease
    2. Data Mining
    3. Machine Learning
    4. Medical Informatics
    5. Supervised Learning

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    • (2025)Novel framework of significant risk factor identification and cardiovascular disease predictionExpert Systems with Applications10.1016/j.eswa.2024.125678263(125678)Online publication date: Mar-2025
    • (2024)A Hybrid System Based on Bayesian Networks and Deep Learning for Explainable Mental Health DiagnosisApplied Sciences10.3390/app1418828314:18(8283)Online publication date: 14-Sep-2024
    • (2024)Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinementFrontiers in Medicine10.3389/fmed.2024.140737611Online publication date: 5-Jul-2024
    • (2024)Machine Learning Model Enabled with Data Optimisation for Prediction of Coronary Heart Disease2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies10.1109/TQCEBT59414.2024.10545194(1-5)Online publication date: 22-Mar-2024
    • (2024)Heart Disease Diagnosis by Machine Learning Techniques2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630058(1-7)Online publication date: 2-Apr-2024
    • (2024)Cardiovascular Disease Prediction Using Machine Learning Techniques: A Review2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10629699(1-6)Online publication date: 2-Apr-2024
    • (2024)Coronary Artery Disease Detection in Early Stages Using Machine Learning2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593495(1-7)Online publication date: 24-May-2024
    • (2024)Efficient Early Detection of Cardiovascular Disease from ECG Imaging Using a One-Stage Deep Learning Model2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)10.1109/ICUIS64676.2024.10866836(581-588)Online publication date: 12-Dec-2024
    • (2024)Exploring the Potential of Machine Learning and Deep Learning in ECG Image Analysis for Cardiovascular Disease Diagnosis2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC60852.2024.10689932(1344-1349)Online publication date: 7-Aug-2024
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