Indian Journal of Computer Science

Open Access Open Access  Restricted Access Subscription Access

A Review of Role of Machine Learning Models in Coronary Heart Disease Detection Accuracy


Affiliations
1 Assistant Professor & Research Scholar, Department of CSE, Vivekananda Global University, Jaipur - 303 012, Rajasthan, India
2 Assistant Professor, Department of CSE, Vivekananda Global University, Jaipur - 303 012, Rajasthan, India
3 Assistant Professor, Department of CSE, CMR College of Engineering & Technology, Medchal, Hyderabad, Telangana - 501 401, India
4 HOD, Department of Computer Science, Vivekananda Global University, Jaipur, Rajasthan - 303 012, India

   Subscribe/Renew Journal


According to the World Health Organization, heart disease is the most widespread disease in the world, affecting over a billion people. Generally, the lifestyles of people are occasionally plagued by stress, worry, and sadness, among other things. The early detection of this condition is tough, and it is a difficult task in medical science. The goal of this research is to better understand the detection accuracy of particular machine learning models (MLMs), as well as their limitations and categorization strategies. Many researchers used classification techniques such as Naive Bays (NB), decision trees (DT), Cooperative Neural-Network Ensembles (CNNEs), logistic regression (LR), Support Vector Machine (SVM), Least Square Twin Support Vector Machine (LS-SVM), k-Nearest Neighbor (KNN), Bays Net (BN), Artificial Neural Network (ANN), and Multi-Layer Perception (MLP) (MLP). In total, the dataset contains more than 50 features attributes. To boost accuracy, the study uses different feature selection approaches to identify the most appropriate features for detecting the disease. The present study achieved a maximum classification accuracy of 96.29%, and there is a need to improve accuracy in the shortest period possible by developing single MLMs for detecting and selecting specific features. Many studies employ hybrid approaches to improve the accuracy of percentages by layering two or more classification algorithms (based on specified symptoms and traits of a human being). It is not always more efficient and time-consuming. As a result, flexible MLMs with feature selection and reduction strategies are required. Further, the current research focuses on boosting accuracy and includes future viewpoints or uses of research as well.

Keywords

Artificial Neural Network, Cooperative Neural-Network Ensembles, K-Nearest Neighbor, Least Square Twin Support Vector Machine, Multi-Layer Perception, Naive Bays, Support Vector Machine.
User
Subscription Login to verify subscription
Notifications
Font Size

  • A. T. Sayad and P. P. Halkarnikar, "Diagnosis of heart disease using neural network approach," Int. J. Advances Sci. Eng. Technol., vol. 2, no. 3, pp. 88–92, Jul. 2014. http://www.iraj.in/journal/journal_file/journal_pdf/6-71-140490825388-92.pdf

  • A. Taneja, "Heart disease prediction system using data mining techniques," Oriental J. Comput. Sci. Technol., vol. 6, no. 4, pp. 457–466, Dec. 2013.

  • A. Methaila, P. Kansal, H. Arya, and P. Kumar, "Early heart disease prediction using data mining techniques," in 7th Int. Conf. Comput. Sci., Eng. Inform. Technol. (CCSEIT 2017), Comput. Sci. Inform. Technol., pp. 53–59, 2014. Online.. Available: https://airccj.org/CSCP/vol4/csit42607.pdf

  • B. A. Jabbar, B. L Deekshatulu, and P. Chandra, "Classification of heart disease using artificial neural network and feature subset selection," Global J. Comput. and Technol. Neural Artif. Intell., vol. 13, no. 3, pp. 5–14, 2013. Online.. Available: https://core.ac.uk/download/pdf/231159766.pdf

  • C. S. Prakash, M. Madhu Bala, and A. Rudra, "Data Sci. framework - Heart disease predictions, variant models and visualizations," in 2020 Int. Conf. Comput. Sci., Eng.Appl., 2020, pp. 1–4, doi: 10.1109/ICCSEA49143.2020.9132920

  • C. -H. Lin, P.-K. Yang, Y. -C. Lin and P. -K. Fu, “On machine learning models for heart disease diagnosis,” in 2nd IEEE Eurasia Conf. Biomed. Eng., Healthcare Sustainability, 2020.

  • H. Das, B. Naik, and H. S. Behera, "A hybrid neuro-fuzzy and feature reduction model for classification," Advances Fuzzy Syst., vol. 2020, Article ID 4152049, pp. 1–15, 2020, doi:10.1155/2020/4152049

  • D. Vadicherla and S. Sonawane, "Classification of heart disease using SVM and ANN," Int. J. Res. Comput. Commun. Technol., vol. 2, no. 9, pp. 694–701, Sep. 2013.

  • M. Durairaj and V. Revathi, "Prediction of heart disease using back propagation MLP algorithm," Int. J. Scientific Technol. Res., vol. 4, no. 8, pp. 235–239, Aug. 2015.

  • H. Murthy and M. Meenakshi, "ANN model to predict coronary heart disease based on risk factors," Bonfring Int. J. Man-Mach. Interface, vol. 3, no.2, pp. 13–18, Jun. 2013.

  • W. H. Hong, J. H Yap, and G. Selvachandran, P. H. Thong, and L. H. Son, “Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest,” Complex Intell. Syst., vol. 7, pp. 163–189, 2021. doi:10.1007/s40747-020-00185-w

  • J.Banupriya and S. Kiruthika, "Heart disease using data mining algorithm on neural networks and genetic algorithm," Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 6, no. 8, pp. 40–42, Aug. 2016.

  • K. S. Krishnasree and M. R. N. Rao, "Diagnosis of heart disease using neural networks - Comparative study of Bayesian regularization with multiple regression model," J. Theor. Appl. Inform. Technol., vol. 88, no. 3, pp. 638–643, Jun. 2016.

  • K. Vinay R., K. L. S. Soujanya, and P. Singh, “Disease prediction by using deep learning based on patient treatment history,” Int. J. Recent Technol. Eng., vol. 7, no. 6, pp. 1159–1168, Mar. 2019.

  • C. M. Latha and K. L. S. Soujanya, “Enhancing end-to-end device security of internet of things using dynamic cryptographic algorithm,” Int. J. Civil Eng. Technol. vol. 9, no. 9, pp. 408–415, 2018.

  • L. Verma, S. Srivastava, and P. C. Negi, “An intelligent noninvasive model for coronary artery disease detection,” Complex Intell. Syst., 4, pp. 11–18, 2018, doi: 10.1007/s40747-017-0048-6

  • C. M. Latha and K. L. S. Soujanya, “Secure IoT Framework Through FSIE Approach,” in Singh P. K., Veselov G., Vyatkin V., Pljonkin A., Dodero J. M., Kumar Y. (eds), “Futuristic trends in network and communication technologies,” FTNCT 2020. Commun. Comput. Inf. Sci., vol. 1395, Springer, Singapore, 2021, doi: 10.1007/978-981-16-1480-4_2

  • M. G. Tsipouras, T. P. Exarchos, D. I. Fotiadis, A. P. Kotsia, K. V. Vakalis, K. K. Naka, and Lampros K. Michalis, "Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling," IEEE Trans. Inf. Technol. Biomedicine, vol. 12, no. 4, pp. 447–458, 2008, doi: 10.1109/TITB.2007.907985

  • M. A. M. Abushariah, A. A. M. Alqudah, O. Y. Adwan, and R. M. M. Yousef," Autom. heart disease diagnosis system based on Artif. Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Syst. (ANFIS) approaches," J. Softw. Eng. Appl., 7, pp. 1055–1064, 2014.

  • N. A. Saeed and Z. T. M. Al-Ta'I, "Feature selection using hybrid dragonfly algorithm in a heart disease predication system," Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 2862–2867, 2019, doi:10.35940/ijeat.f8786.088619

  • P. Bajaj and P. Gupta, "Review on heart disease diagnosis based on datamining techniques," Int. J. Sci. Res., vol. 3, no. 5, pp. 1593-1596, May 2014.

  • R. J. P. Princy, S. Parthasarathy, P. S. H. Jose, A. R. Lakshminarayanan, and S. Jeganathan, “Prediction of cardiac disease using Supervised Machine Learning Algorithms,” Proc. Int. Conf. Intell. Comput. Control Sys., May 13–15, 2020.

  • S. Florence, N. G. B. Amma, G. Annapoorani, and K. Malathi, "Predicting the risk of heart attacks using neural network and decision tree," Int. J. Innovative Res. Comput. Commun. Eng., vol. 2, no. 11, pp. 7025–7030, Nov. 2014.

  • S. Kamley, "Performance of hybrid ensemble classification techniques for prevalence of heart disease prediction," Int. J. Innovative Technol. Exploring Eng., vol. 8, no. 10, pp. 1875–1882, Aug. 2019, doi:10.35940/ijitee.j9233.0881019

  • S. S. Sanagala, S. K. Gupta, V. K. Koppula, and M. Agarwal, “A fast and light weight deep convolution neural network model for cancer disease identification in human lung(s),” in 2019 18th IEEE Int. Conf. Mach. Learn. Appl., (ICMLA) 2019, Boca Raton, FL, USA, Dec. 16–19, 2019, pp. 1382–1387, doi: 10.1109/ICMLA.2019.00225

  • S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” in IEEE Access, vol. 7, pp. 81542–81554,2019, doi: 10.1109/ACCESS.2019.2923707

  • S. Grampurohit and C. Sagarnal, "Disease prediction using machine learning algorithms," in 2020 Int. Conf. Emerg. Technol. (INCET), 2020, pp. 1–7, doi: 10.1109/INCET49848.2020.9154130

  • T. K. Agrawal, “Neural network & Naïve Bays based hybrid model for heart disease diagnosis,” in 12th Biyani Int. Conf. (BICON-2017), 2017.

  • X. Liu, X. Wang, Q. Su, M. Zhang, Y. Zhu, Q. Wang, and Q. Wang, “A hybrid classification system for heart disease diagnosis based on the RFRS method,” Comput. Math. Methods Medicine, vol. 2017, Art. No. 8272091, pp. 1–11, doi: 10.1155/2017/8272091

  • X. Wenxin, "Heart disease prediction model based on model ensemble," in 2020 3rd Int. Conf. Artif. Intell. Big Data (ICAIBD), 2020, pp. 195–199, doi: 10.1109/ICAIBD49809.2020.9137483

  • Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, and A. A. Yarifard, “Computer aided decision making for heart disease detection using hybrid neural network - Genetic algorithm,” Comput. Methods Programs Biomedicine, vol. 141, pp. 19–26, Apr. 2017, doi: 10.1016/j.cmpb.2017.01.004

  • D. Tomar and S. Agarwal, "Feature selection based least square win support vector machine for diagnosis of heart disease,” Int. J. Adv. Sci. Technol., vol. 65, pp. 39–58, 2014, doi: 10.14257/IJBSBT.2014.6.2.07

  • H. Das, B. Naik, and H. S. Behera, “Medical disease analysis using neuro-fuzzy with feature extraction model for classification,” Informatics in Medicine Unlocked, vol. 18, 2020, doi: 10.1016/j.imu.2019.100288

  • S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2923707


Abstract Views: 146

PDF Views: 0




  • A Review of Role of Machine Learning Models in Coronary Heart Disease Detection Accuracy

Abstract Views: 146  |  PDF Views: 0

Authors

Tarun Kumar Agarwal
Assistant Professor & Research Scholar, Department of CSE, Vivekananda Global University, Jaipur - 303 012, Rajasthan, India
Hemant Sharma
Assistant Professor, Department of CSE, Vivekananda Global University, Jaipur - 303 012, Rajasthan, India
Challa Madhavi Latha
Assistant Professor, Department of CSE, CMR College of Engineering & Technology, Medchal, Hyderabad, Telangana - 501 401, India
Sitaram Gupta
HOD, Department of Computer Science, Vivekananda Global University, Jaipur, Rajasthan - 303 012, India

Abstract


According to the World Health Organization, heart disease is the most widespread disease in the world, affecting over a billion people. Generally, the lifestyles of people are occasionally plagued by stress, worry, and sadness, among other things. The early detection of this condition is tough, and it is a difficult task in medical science. The goal of this research is to better understand the detection accuracy of particular machine learning models (MLMs), as well as their limitations and categorization strategies. Many researchers used classification techniques such as Naive Bays (NB), decision trees (DT), Cooperative Neural-Network Ensembles (CNNEs), logistic regression (LR), Support Vector Machine (SVM), Least Square Twin Support Vector Machine (LS-SVM), k-Nearest Neighbor (KNN), Bays Net (BN), Artificial Neural Network (ANN), and Multi-Layer Perception (MLP) (MLP). In total, the dataset contains more than 50 features attributes. To boost accuracy, the study uses different feature selection approaches to identify the most appropriate features for detecting the disease. The present study achieved a maximum classification accuracy of 96.29%, and there is a need to improve accuracy in the shortest period possible by developing single MLMs for detecting and selecting specific features. Many studies employ hybrid approaches to improve the accuracy of percentages by layering two or more classification algorithms (based on specified symptoms and traits of a human being). It is not always more efficient and time-consuming. As a result, flexible MLMs with feature selection and reduction strategies are required. Further, the current research focuses on boosting accuracy and includes future viewpoints or uses of research as well.

Keywords


Artificial Neural Network, Cooperative Neural-Network Ensembles, K-Nearest Neighbor, Least Square Twin Support Vector Machine, Multi-Layer Perception, Naive Bays, Support Vector Machine.

References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi1%2F168955