Abstract
Heart disease is the top severe reason for death in the world. However, the prediction of this disease is a censorious challenge in clinical data analysis. Various traditional and recent approaches have shown efficacy in portraying the disease prediction and diagnosis but have restricted performance due to insufficient data modeling. Therefore, the forecast of heart-related diseases needs and involves a more accurate solution. To assist the decision-making towards heart disease diagnosis, intelligent techniques such as machine learning (ML) may be helpful from the large quantity of data produced by the healthcare industry. In this work, we process and compute the accuracy of several standard ML algorithms, namely Decision Tree, K Nearest Neighbor, Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine, and XGBoost classifier for predicting heart disease for using a standard dataset for training and testing and finally, the performance will be assessed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
WHO. Cardiovascular Diseases Report by World Health Organization (2021). https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
Challa, S.K., Kumar, A., Semwal, V.B.: A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. Vis. Comput. 0123456789 (2021). https://doi.org/10.1007/s00371-021-02283-3
Dua, N., Singh, S.N., Semwal, V.B.: Multi-input CNN-GRU based human activity recognition using wearable sensors. Computing 103(7), 1461–1478 (2021). https://doi.org/10.1007/s00607-021-00928-8
Dewangan, D.K., Sahu, S.P., Sairam, B., Agrawal, A.: VLDNet: vision-based lane region detection network for intelligent vehicle system using semantic segmentation. Computing 103(12), 2867–2892 (2021). https://doi.org/10.1007/s00607-021-00974-2
Singh, A., Bansal, A., Chauhan, N., Sahu, S.P., Dewangan, D.K.: Image generation using GAN and its classification using SVM and CNN. In: Proceedings of Emerging Trends and Technologies on Intelligent Systems. ETTIS 2021. Advances in Intelligent Systems and Computing, vol. 1371. Springer, Singapore (2021)
Ojha, A., Sahu, S.P., Dewangan, D.K.: Vehicle detection through instance segmentation using mask R-CNN for intelligent vehicle system. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 954–959 (2021). https://doi.org/10.1109/ICICCS51141.2021.9432374
Dewangan, D.K., Sahu, S.P.: Driving behaviour analysis of intelligent vehicle system for lane detection using vision-sensor. IEEE Sens. J. 21(5), 6367–6375 (2021). https://doi.org/10.1109/JSEN.2020.3037340
Sahu, S.P., Dewangan, D.K., Agrawal, A., Sai Priyanka, T.: Traffic light cycle control using deep reinforcement technique. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 697–702 (2021). https://doi.org/10.1109/ICAIS50930.2021.9395880
Dewangan, D.K., Sahu, S.P.: Deep learning-based speed bump detection model for intelligent vehicle system using raspberry pi. IEEE Sens. J. 21(3), 3570–3578 (2021). https://doi.org/10.1109/JSEN.2020.3027097
Dewangan, D.K., Sahu, S.P.: Predictive control strategy for driving of intelligent vehicle system against the parking slots. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 10–13 (2021). https://doi.org/10.1109/ICICCS51141.2021.9432362
Pandey, P., Dewangan, K.K., Dewangan, D.K.: Enhancing the quality of satellite images using fuzzy inference system. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 (2018). https://doi.org/10.1109/ICECDS.2017.8390024
Pandey, P., Dewangan, K.K., Dewangan, D.K.: Enhancing the quality of satellite images by preprocessing and contrast enhancement. Proceedings of the 2017 IEEE Intermnational Conference on Communication Signal Processing, ICCSP 2017, January 2018, pp. 56–60 (2018). https://doi.org/10.1109/ICCSP.2017.8286525
Dewangan, D.K., Rathore, Y.: Image quality estimation of images using full reference and no reference method. Int. J. Adv. Res. Comput. Sci. 2(5), 323–326 (2011)
Sahu, S.P., Londhe, N.D., Verma, S.:Pulmonary nodule detection in CT images using optimal multilevel thresholds and rule-based filtering. IETE J. Res. 1–18 (2019). https://doi.org/10.1080/03772063.2019.1603084
Waiker, D., Das Baghel, P., Varma, K.R., Sahu, S.P.: Effective semantic segmentation of lung X-ray images using U-Net architecture. In: Proceedings of the 4th International Conference on Computer Methodolgy Communication, ICCMC 2020, pp. 603–607 (2020). https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000112
Sahu, S.P., Londhe, N.D., Verma, S., Singh, B.K., Banchhor, S.K.: Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine-learning paradigm. JInt. J. Imaging Syst. Technol. 31(3), 1503–1518 (2021). https://doi.org/10.1002/ima.22539
Barik, S., Mohanty, S., Rout, D., Mohanty, S., Patra, A.K., Mishra, A.K.: Heart disease prediction using machine learning techniques. In: Pradhan, G., Morris, S., Nayak, N. (eds.) Advances in Electrical Control and Signal Systems. LNEE, vol. 665, pp. 879–888. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-5262-5_67
Shah, D., Patel, S., Bharti, S.K.: Heart disease prediction using machine learning techniques. In: Proceedings of the IEEE 2020 2nd International Conference Advanced Computer Communication Control Networking, ICACCCN 2020, vol. 1, no. 6, pp. 177–181 (2020). https://doi.org/10.1109/ICACCCN51052.2020.9362842
Swain, D., Ballal, P., Dolase, V., Dash, B., Santhappan, J.: An efficient heart disease prediction system using machine learning. In: Swain, D., Pattnaik, P.K., Gupta, P.K. (eds.) Machine Learning and Information Processing. AISC, vol. 1101, pp. 39–50. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1884-3_4
Abdeldjouad, F.Z., Brahami, M., Matta, N.: A hybrid approach for heart disease diagnosis and prediction using machine learning techniques. In: Jmaiel, M., Mokhtari, M., Abdulrazak, B., Aloulou, H., Kallel, S. (eds.) ICOST 2020. LNCS, vol. 12157, pp. 299–306. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51517-1_26
Lafta, R., Zhang, J., Tao, X., Li, Y., Diykh, M., Lin, J.-W.: A structural graph-coupled advanced machine learning ensemble model for disease risk prediction in a telehealthcare environment. In: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (eds.) Big Data in Engineering Applications. SBD, vol. 44, pp. 363–384. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8476-8_18
Kumar, A., Gyawali, R., Agarwal, S.: Cardiovascular disease prediction using machine learning tools. In: Agarwal, S., Verma, S., Agrawal, D.P. (eds.) MISP 2019. AISC, vol. 1085, pp. 441–451. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1366-4_35
Sajeev, S., et al.: Deep learning to improve heart disease risk prediction. In: Liao, H., et al. (eds.) MLMECH/CVII-STENT -2019. LNCS, vol. 11794, pp. 96–103. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33327-0_12
Bhuvaneeswari, R., Sudhakar, P., Narmadha, R.P.: Machine learning based optimal data classification model for heart disease prediction. In: Hemanth, D.J., Shakya, S., Baig, Z. (eds.) ICICI 2019. LNDECT, vol. 38, pp. 485–491. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34080-3_55
Kannan, R., Vasanthi, V.: Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. In: Soft Computing and Medical Bioinformatics. SAST, pp. 63–72. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0059-2_8
Krstačič, G., Gamberger, D., Šmuc, T.: Coronary heart disease patient models based on inductive machine learning. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 113–116. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-48229-6_15
Singh, Y.K., Sinha, N., Singh, S.K.: Heart disease prediction system using random forest. In: Singh, M., Gupta, P.K., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds.) ICACDS 2016. CCIS, vol. 721, pp. 613–623. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5427-3_63
Maji, S., Arora, S.: Decision tree algorithms for prediction of heart disease. In: Fong, S., Akashe, S., Mahalle, P.N. (eds.) Information and Communication Technology for Competitive Strategies. LNNS, vol. 40, pp. 447–454. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0586-3_45
Janosi, A., Steinbrunn, W., Pfisterer, M., Detrano, R.: Heart Disease Data Set, UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Heart+Disease
Dewangan, D.K., Sahu, S.P.: RCNet: road classification convolutional neural networks for intelligent vehicle system. Intel. Serv. Robot. 14(2), 199–214 (2021). https://doi.org/10.1007/s11370-020-00343-6
Dewangan, D.K., Sahu, S.P.: PotNet: pothole detection for autonomous vehicle system using convolutional neural network. Electron. Lett. 57(2), 53–56 (2021). https://doi.org/10.1049/ell2.12062
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, V., Arora, A. (2022). Performance Assessment for Heart-Disease Prediction Using Machine Learning Algorithms. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_23
Download citation
DOI: https://doi.org/10.1007/978-981-19-2719-5_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2718-8
Online ISBN: 978-981-19-2719-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)