HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm.
DOI:
https://doi.org/10.9781/ijimai.2021.10.003Keywords:
ABGNB Algorithm, Heart Disease Prediction, Machine Learning, Recursive Feature Elimination, UCI Heart Disease DatasetAbstract
Heart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-support systems based on machine learning (ML) algorithms. However, the lower prediction accuracy is the main issue in these heart disease decisionsupport systems. The proposed work developed a heart disease decision-support system (HDDSS) that can predict whether or not a person has heart disease. The main goal of this research work is to use the RFEABGNB to improve HDDSS prediction accuracy. The Cleveland heart disease dataset is used for training and validating the proposed HDDSS. The two significant stages of HDDSS are the feature election stage and the classification modeling stage. The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset. In the second stage of HDDSS, the proposed Adaptive boosted Gaussian Naïve Bayes (ABGNB) algorithm has been used to construct a classification model for training and validating a heart disease decision-support system. An output of HDDSS is analyzed using various classification output measures. According to the results obtained, our proposed method attained a predictive performance of 92.87 percent. This HDDSS model would perform well when compared to other heart disease decision-support systems found in the literature. According to our experimental analysis, the RFE-ABGNB focused heart disease decision-support system is more appropriate for a heart disease prediction.
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Anand, Sonia S., Shofiqul Islam, Annika Rosengren, Maria Grazia Franzosi, Krisela Steyn, Afzal Hussein Yusufali, Matyas Keltai, Rafael Diaz, Sumathy Rangarajan, and Salim Yusuf, “Risk factors for myocardial infarction in women and men: insights from the INTERHEART study,” European heart journal, vol. 29, no. 7, pp. 932-940, 2008.
Frohlich, Edward D., and Patrick J. Quinlan, “Coronary heart disease risk factors: public impact of initial and later-announced risks,” The Ochsner Journal, vol. 14, no. 4, 532-537, 2014.
Wah, Teh Ying, Ram Gopal Raj, and Uzair Iqbal, “Automated diagnosis of coronary artery disease: a review and workflow,” Cardiology research and practice, 2018.
Ali, Liaqat, Atiqur Rahman, Aurangzeb Khan, Mingyi Zhou, Ashir Javeed, and Javed Ali Khan, “An Automated Diagnostic System for Heart Disease Prediction Based on χ2 Statistical Model and Optimally Configured Deep Neural Network,” IEEE Access, vol. 7, pp. 34938-34945, 2019.
Haq, Amin Ul, Jian Ping Li, Muhammad Hammad Memon, Shah Nazir, and Ruinan Sun, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mobile Information Systems, 2018.
Otoom, Ahmed Fawzi, Emad E. Abdallah, Yousef Kilani, Ahmed Kefaye, and Mohammad Ashour, “Effective diagnosis and monitoring of heart disease,” International Journal of Software Engineering and Its Applications, vol. 9, no. 1, pp. 143-156, 2015.
Mohan, Senthilkumar, Chandrasegar Thirumalai, and Gautam Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542-81554, 2019.
Li, Jian Ping, Amin Ul Haq, Salah Ud Din, Jalaluddin Khan, Asif Khan, and Abdus Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, pp. 107562- 107582, 2020.
Chen, Austin H., Shu-Yi Huang, Pei-Shan Hong, Chieh-Hao Cheng, and En-Ju Lin. “HDPS: Heart disease prediction system.” In 2011 computing in cardiology, IEEE, 2011, pp. 557-560.
Takci, Hidayet, “Improvement of heart attack prediction by the feature selection methods,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, no. 1, pp. 1-10, 2018.
Reddy, G. Thippa, and Neelu Khare, “An efficient system for heart disease prediction using hybrid OFBAT with rule-based fuzzy logic model,” Journal of Circuits, Systems and Computers, vol. 26, no. 04, 2017.
Hossin, Mohammad, and M. N. Sulaiman, “A review on evaluation metrics for data classification evaluations,” International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, 2015.
Janosi A, Steinbrunn W, Pfisterer M, Detrano R. Heart Disease UCI. Available online: https://www.kaggle.com/ronitf/heart-disease-uci (Cited on 23 Jan 2021).
Khalid, Samina, Tehmina Khalil, and Shamila Nasreen. “A survey of feature selection and feature extraction techniques in machine learning.” In 2014 Science and Information Conference, IEEE, 2014, pp. 372-378.
Miao, Jianyu, and Lingfeng Niu, “A survey on feature selection,” Procedia Computer Science, vol. 91, pp. 919-926, 2016.
Guyon, Isabelle, and André Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, no. Mar 2003, pp. 1157-1182, 2003.
Massey, Adam, and Steven J. Miller. “Tests of hypotheses using statistics.” Mathematics Department, Brown University, Providence, RI 2912 2006, pp. 1-32.
Murphy, Kevin P. “A Probabilistic Perspective.” Text book (2012).
Hoffman, J. I. E. “Logistic regression.” Basic Biostatistics for Medical and Biomedical Practitioners, 2019, pp. 581-589.
Perez, Aritz, Pedro Larranaga, and Inaki Inza, “Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes,” International Journal of Approximate Reasoning, vol. 43, no. 1, pp. 1-25, 2006.
Freund, Yoav, and Robert E. Schapire. “Experiments with a new boosting algorithm.” In icml, vol. 96, 1996, pp. 148-156.
Kohavi, Ron. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” In Ijcai, vol. 14, no. 2, 1995, pp. 1137-1145.
Syarif, Iwan, Adam Prugel-Bennett, and Gary Wills, “SVM parameter optimization using grid search and genetic algorithm to improve classification performance,” Telkomnika, vol. 14, no. 4, pp. 1502-1509, 2016.
Wu, Jia, Xiu-Yun Chen, Hao Zhang, Li-Dong Xiong, Hang Lei, and Si-Hao Deng, “Hyperparameter optimization for machine learning models based on Bayesian optimization,” Journal of Electronic Science and Technology, vol. 17, no. 1, pp. 26-40, 2019.
Kumar, Vipin, and Sonajharia Minz, “Feature selection: a literature review,” SmartCR, vol. 4, no. 3, pp. 211-229, 2014.
Jović, Alan, Karla Brkić, and Nikola Bogunović. “A review of feature selection methods with applications.” In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), IEEE, 2015, pp. 1200-1205.
Rish, Irina “An empirical study of the naive Bayes classifier.” In IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3, no. 22, 2001, pp. 41-46.
Tan, Songbo, “An effective refinement strategy for KNN text classifier,” Expert Systems with Applications, vol. 30, no. 2, pp. 290-298, 2006.
Cortes, Corinna, and Vladimir Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273-297, 1995.
Safavian, S. Rasoul, and David Landgrebe, “A survey of decision tree classifier methodology,” IEEE transactions on systems, man, and cybernetics, vol. 21, no. 3, pp. 660-674, 1991.
David, H. B. F., and Belcy, S. A., “Heart Disease Prediction Using Data Mining Techniques,” ICTACT Journal on Soft Computing, vol. 9, no. 1, 2018.
Das, Sumit, Manas Kumar Sanyal, and Sourav Kumar Upadhyay. “A Comparative Study for Prediction of Heart Diseases Using Machine Learning.” Available at SSRN 3526776, 2020.
Garg, A., Sharma, B. and Khan, R., “Heart disease prediction using machine learning techniques.” In IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2021: Vol. 1022, No. 1, p. 012046.
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