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Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases

Authors:

Mirjam Jonkman,

MD. Zahid HasanAuthors Info & Claims

ICISDM '21: Proceedings of the 2021 5th International Conference on Information System and Data Mining

Pages 14 - 20

https://doi.org/10.1145/3471287.3471297

Published: 25 September 2021 Publication History

Abstract

Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K- Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by “Pearson's Correlation” technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. The best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. This could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%.

CCS CONCEPTS • Applied computing • Life and medical sciences • Health informatics

References

[1]

“WHO: global cause of death due to heart disease,” Available: who.int/cardiovascular_diseases/resources/atlas/en/, Accessed: 10/7/20.

[2]

R. Detrano, “International application of a new probability algorithm for the diagnosis of coronary artery disease,” Am. J. Cardiol., vol. 64, no. 5, pp. 304–310, 1989.

[3]

H. Kahramanli and N. Allahverdi, “Design of a hybrid system for the diabetes and heart diseases,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 82–89, 2008.

Digital Library

[4]

S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniques,” in Proceedings of IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 2008), pp. 108–115, Doha, Qatar, March-April 2008.

Digital Library

[5]

E. O. Olaniyi and O. K. Oyedotun, “Heart diseases diagnosis using neural networks arbitration,” International Journal of Intelligent Systems and Applications, vol. 7, no. 12, pp. 75–82, 2015.

[6]

R. Das, I. Turkoglu, and A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles,” Expert Systems with Applications, vol. 36, no. 4, pp. 7675–7680, 2009.

Digital Library

[7]

M. A. Jabbar, B. L. Deekshatulu, and P. Chandra, “Classification of heart disease using artificial neural network and feature subset selection,” Global Journal of Computer Science and Technology Neural & Artificial Intelligence, vol. 13, no. 11, 2013.

[8]

O. W. Samuel, G. M. Asogbon, A. K. Sangaiah, P. Fang, and G. Li, “An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction,” Expert Systems with Applications, vol. 68, pp. 163–172, 2017.

Digital Library

[9]

J. H. Gennari, P. Langley, and D. Fisher, “Models of incremental concept formation,” Artif. Intell., vol. 40, no. 1–3, pp. 11–61, 1989.

Digital Library

[10]

K. Bache and M. Lichman, “UCI Machine Learning Repository,” University of California Irvine, vol. 2008, no. 14/8. 2013.

[11]

A. R. Beeravolu, S. Azam, M. Jonkman, B. Shanmugam, K. Kannoorpatti and A. Anwar, "Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN," IEEE Access, vol. 9. 2021.

[12]

P. Ghosh, S. Azam, M. Jonkman, A. Karim, "Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques," in IEEE Access, vol. 9, 2021.

[13]

M. M. Alam, S. Saha, P. Saha, F. N. Nur, N. N. Moon, A. Karim, and S. Azam, “D-CARE: A Non-invasive Glucose Measuring Technique for Monitoring Diabetes Patients”, Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, vol. 1, pp. 443-453, 2019.

[14]

Hegelich, ‘‘Decision trees and random forests: Machine learning techniques to classify rare events, ’’Eur. Policy Anal. 2(1), 2016.

[15]

L. Y. Hu, M.-W. Huang, S.-W. Ke, and C.-F. Tsai, ‘‘The distance function effect on K-Nearest Neighbor classification for medical datasets, ’’Springer-Plus, vol. 5, no. 1, p. 1304, Aug. 2016

[16]

P. Ghosh, A. Karim, S. T. Atik, S. A., M. Saifuzzaman," Expert Model of Cancer Disease Using Supervised Algorithms with a LASSO Feature Selection Approach," International Journal of Electrical and Computer Engineering, Vol. 11 (3), 2020.

[17]

“UCI machine learning repository: Chronic kidney disease data set,” archive.ics.uci.edu/ml/datasets/heart+disease, Accessed on 10/7/20.

[18]

“Smarter ways to encode categorical data for machine learning,” https://towardsdatascience.com/smarter-ways-to-encode-categoricaldata-for-machine-learning-part-1-of-3-6dca2f71b159, (Accessed on 10/07/2020).

[19]

“A Quick Introduction on Pearson Correlations,” Available at. www.spss-tutorials.com/, Accessed on 10/07/2020).

[20]

S. Oehmcke, O. Zielinski and O. Kramer, “KNN ensembles with penalized DTW for multivariate time series imputation”, International Joint Conference on Neural Networks (IJCNN), 2016.

[21]

“Standard Scaler Approach of Machine Learning,” Available: https://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html, (Accessed on 25/07/2020).

Cited By

R LR SP KS N(2024)Premature Discovery of Long-Term Prolonged Renal Disorder Using Machine Learning Algorithm2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493410(1-4)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493410
Bansla VSaini R(2024)Utilizing a Machine Learning Approach, Chronic Disease Identification and Prediction2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)10.1109/IC3SE62002.2024.10592902(815-819)Online publication date: 9-May-2024
https://doi.org/10.1109/IC3SE62002.2024.10592902
Qi TZaki F(2024)Optimizing Heart Disease Prediction: Integrating Relief and Logistic Regression for Feature Selection2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS63860.2024.10730361(24-29)Online publication date: 3-Sep-2024
https://doi.org/10.1109/AiDAS63860.2024.10730361
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ICISDM '21: Proceedings of the 2021 5th International Conference on Information System and Data Mining

May 2021

162 pages

ISBN:9781450389549

DOI:10.1145/3471287

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 September 2021

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ICISDM 2021

ICISDM 2021: 2021 the 5th International Conference on Information System and Data Mining

May 27 - 29, 2021

CA, Silicon Valley, USA

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Cited By

R LR SP KS N(2024)Premature Discovery of Long-Term Prolonged Renal Disorder Using Machine Learning Algorithm2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493410(1-4)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493410
Bansla VSaini R(2024)Utilizing a Machine Learning Approach, Chronic Disease Identification and Prediction2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)10.1109/IC3SE62002.2024.10592902(815-819)Online publication date: 9-May-2024
https://doi.org/10.1109/IC3SE62002.2024.10592902
Qi TZaki F(2024)Optimizing Heart Disease Prediction: Integrating Relief and Logistic Regression for Feature Selection2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS63860.2024.10730361(24-29)Online publication date: 3-Sep-2024
https://doi.org/10.1109/AiDAS63860.2024.10730361
Atimbire SAppati JOwusu E(2024)Empirical exploration of whale optimisation algorithm for heart disease predictionScientific Reports10.1038/s41598-024-54990-114:1Online publication date: 24-Feb-2024
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Hamadneh SAkourm IAl Kurdi BAlzoubi HAlshurideh MAlHamad A(2024)An IoMT-Based Healthcare Model to Monitor Elderly People Using Transfer LearningCyber Security Impact on Digitalization and Business Intelligence10.1007/978-3-031-31801-6_16(267-279)Online publication date: 4-Jan-2024
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Shambharkar SMoon PBainalwar PBoarkar S(2023)Machine Learning-Based Approach for Early Detection and Prediction of Chronic Diseases2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI)10.1109/IDICAIEI58380.2023.10406914(1-8)Online publication date: 27-Nov-2023
https://doi.org/10.1109/IDICAIEI58380.2023.10406914
Freeda ST NSelvan T(2023)Evaluating the Effective Machine Learning Techniques for Early Prediction of Heart Disease2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA58529.2023.10394798(703-708)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICECA58529.2023.10394798
Kumari NGautam M(2023)Chronic Disease prediction using Machine Learning Techniques: A Survey2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307746(1-9)Online publication date: 6-Jul-2023
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Inayath SKanakala S(2023)Detection of Chronic Kidney Disease Using Machine Learning Algorithms2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N60023.2023.10541795(825-832)Online publication date: 15-Dec-2023
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