Abstract
Heart disease is considered the most dangerous and fatal infection in the human body. This globally fatal disease cannot be identified easily by a general practitioner, and it requires an analyst or expert to detect it. In the field of medical science, machine learning plays important role in disease prediction to identify the heart infection features. In this perspective, this research work proposes a new technique to predict heart disease by using various classifier algorithms such as random forest, gradient boosting, support vector machine, and K- nearest neighbor algorithms. For this purpose, the classification accuracy and the obtained results of each predictor have been compared. In each analysis, machine learning classifier algorithms: random forest, gradient boosting, support vector machine, and K-nearest neighbor algorithms are used and finally defect, gradient boosting, which has calculated high accuracy with low error values and high correlation value when compared to other used algorithms.
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References
M.S. Amin, Y.K. Chiam, K.D. Varathan, Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 36, 82–93 (2019)
A.K. Verma, S. Pal, S. Kumar, Prediction of skin disease using ensemble data mining techniques and feature selection method—a comparative study. Appl. Biochem. Biotechnol. 190(2), 341–359 (2020)
C.B. Gokulnath, S.P. Shantharajah, An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Comput. 22(6), 14777–14787 (2019)
J.M.T. Wu, M.H. Tsai, Y.Z. Huang, S.H. Islam, M.M. Hassan, A. Alelaiwi, G. Fortino, Applying an ensemble convolutional neural network with Savitzky-Golay filter to construct a phonocardiogram prediction model. Appl. Soft Comput. 78, 29–40 (2019)
A.M. Alaa, T. Bolton, E. Di Angelantonio, J.H. Rudd, M. van Der Schaar, Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423, 604 UK Biobank participants. PloS one 14(5), e0213653 (2019)
A.U. Haq, J.P. Li, M.H. Memon, S. Nazir, R. Sun, A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Info. Syst. 2018, 1–21 (2018)
J. Vijayashree, H.P. Sultana, A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier. Programm. Comput. Softw. 44(6), 388–397 (2018)
T. Vivekanandan, N.C.S.N. Iyengar, Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput. Biol. Med. 90, 125–136 (2017)
N. Khateeb, M. Usman, Efficient heart disease prediction system using K-nearest neighbor classification technique. in Proceedings of the International Conference on Big Data and Internet of Thing, (2017), pp. 21–26
A.K. Ramotra, A. Mahajan, R. Kumar, V. Mansotra, Comparative analysis of data mining classification techniques for prediction of heart disease using the weka and SPSS modeler tools. in Smart Trends in Computing and Communications (Springer, Singapore, 2020), pp. 89–96
S. Narayan, E. Sathiyamoorthy, A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases. Neural Comput. Appl. 31(1), 93–102 (2019)
A.H. Gonsalves, F. Thabtah, R.M.A. Mohammad, G. Singh, Prediction of coronary heart disease using machine learning: an experimental analysis. in Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, 51–56 (2019)
G. Manogaran, R. Varatharajan, M.K. Priyan, Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools Appl. 77(4), 4379–4399 (2018)
V. Jayaraman, H.P. Sultana, Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification. J. Ambient Intell. Humanized Comput. 1–10 (2019)
M. Tanveer, A. Sharma, P.N. Suganthan, Least squares KNN-based weighted multiclass twin SVM. Neurocomputing (2020). https://doi.org/10.1016/j.neucom.2020.02.132
D.C. Yadav, S. Pal, Prediction of heart disease using feature selection and random forest ensemble method. Int J Pharmaceutical Res. 12(4), 56–66 (2020)
H. Lu, S.P. Karimireddy, N. Ponomareva, V. Mirrokni, Accelerating gradient boosting machines. in International Conference on Artificial Intelligence and Statistics (2020) pp. 516–526
B. Richhariya, M. Tanveer, A reduced universum twin support vector machine for class imbalance learning. Pattern Recogn. 102, 107150 (2020)
Yuan, B. H., Liu, G. H.,: Image retrieval based on gradient-structures histogram. Neural Computing and Applications, 1–11 (2020)
M. Alizamir, S. Kim, O. Kisi, M. Zounemat-Kermani, Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables. Hydrol. Sci. J. 65(7), 1173–1190 (2020)
D.C. Yadav, S. Pal, Prediction of thyroid disease using decision tree ensemble method. Human-Intell. Syst. Integra. 1–7 (2020)
M. Baak, R. Koopman, H. Snoek, S. Klous, A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics. Comput. Stat. Data Anal. 152, 107043 (2020)
D.C. Yadav, S. Pal, To generate an ensemble model for women thyroid prediction using data mining techniques. Asian Pac. J. Cancer Prev. 20(4), 1275 (2019)
M.A. Hasan, M.U. Khan, D. Mishra, A computationally efficient method for hybrid EEG-fNIRS BCI based on the pearson correlation. Biomed. Res. Int. 2020, 1–13 (2020)
R. Aggrawal, S. Pal, Sequential feature selection and machine learning algorithm-based patient’s death events prediction and diagnosis in heart disease. SN Comput. Sci. 1, 344 (2020)
A.K. Verma, S. Pal, S. Kumar, Prediction of different classes of skin disease using machine learning techniques. in Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1168. (Springer, Singapore, 2021)
V. Chaurasia, S. Pal, Machine learning algorithms using binary classification and multi model ensemble techniques for skin diseases prediction. Int. J. Biomed. Eng. Technol. 34(1), 57–74 (2020)
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Aggrawal, R., Pal, S. (2021). Prediction of Heart Disease with Different Attributes Combination by Data Mining Algorithms. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_38
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DOI: https://doi.org/10.1007/978-981-33-6862-0_38
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