Amanda H. Gonsalves
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.
- Abdelhamid N., Thabtah F., (2014) Associative Classification Approaches: Review and Comparison. Journal of Information and Knowledge Management (JIKM). Vol. 13, No. 3 (2014) 1450027.Google Scholar
Cross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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_ReviewGoogle Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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%3DihubGoogle ScholarCross Ref
- 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-showsGoogle Scholar
- 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.pdfGoogle Scholar
- Mohammed R., Thabtah F., McCluskey L., (2013) Intelligent Rule based Phishing Websites Classification. Journal of Information Security (2), 1--17. ISSN 17518709. IET.Google Scholar
- Platt, J. C., & Nitschke, R. v. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines.Google Scholar
- Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann Publishers. Google ScholarDigital Library
- 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).Google Scholar
- Sivakumar, S. (n.d.). Prediction of Coronary Heart Disease by learning from retrospective study. Retrieved from GitHub: http://srisai85.github.io/CHD/heart_attack.htmlGoogle Scholar
- 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-preventionGoogle Scholar
- Thabtah F., Peebles D. (2019) A new machine learning model based on induction of rules for autism detection. Health Informatics Journal, 1460458218824711.Google Scholar
- Thabtah F. (2018a) An Accessible and Efficient Autism Screening Method for Behavioural Data and Predictive Analyses. Health Informatics Journal. 19:1460458218796636. 2018.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Thabtah F., Hammoud S (2013) MR-ARM: A MapReduce Association Rule Mining. Journal of Parallel Processing Letter, 23 (3) 1--22, 1350012. World Scientific.Google Scholar
- World Health Organization. (2005). Preventing Chronic Diseases a vital investment. Switzerland: WHO Press.Google Scholar
- 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.Google Scholar
- Yanwei X, W. J. (2007). Combination data mining. Proceedings International Conference on Convergence Information Technology, (pp. 868--872).Google Scholar
Index Terms
- Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis
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