A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning

Shilan Hassan; Maha Sabah Saeed

doi:10.24017/science.2023.2.5

Authors

Shilan Abdullah Hassan Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah , Iraq https://orcid.org/0000-0002-5263-8853
Maha Sabah Saeed Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Iraq https://orcid.org/0000-0002-9218-1376

Abstract

Worldwide, heart attacks, also called myocardial infarctions, are a leading cause of death. Early detection and accurate prediction of heart attacks are crucial for effective medical intervention and patient care. In recent years, machine learning techniques have shown great promise in aiding the diagnosis and prediction of heart attacks. The Organization for World Health (WHO) reports that around 17 million individuals worldwide pass away from cardiovascular diseases (CVD), notably heart attacks and strokes, each year. In this study, 1026 patients, both men and women, are almost equally affected by CVDs. While heart attacks and strokes remain among the leading causes of mortality worldwide, the use of machine learning for predicting heart disease could potentially prevent premature deaths. A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. Its exceptional recall and precision rates highlight its effectiveness in handling challenging classifications. To facilitate the development and deployment of machine learning models, Azure Machine Learning offers a range of tools and services. By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks.

Keywords:

Machine Learning, Heart Disease Prediction, MS Azure Machine, Predictive Analytics, Seaborn

Author Biographies

Shilan Abdullah Hassan, Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah , Iraq

Assistant Lecturer in Network Department
Maha Sabah Saeed, Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Iraq

Assistant Lecturer in Network department

References

M. Chiarito, L. Luceri, A. Oliva, G. Stefanini, and G. Condorelli, “Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold,” European Cardiology Review, Dec. 2022, doi: 10.15420/ecr.2022.11.

C. Krittanawong, H. Zhang, Z. Wang, M. Aydar, and T. Kitai, “Artificial Intelligence in Precision Cardiovascular Medicine,” J Am Coll Cardiol, vol. 69, May 2017, doi: 10.1016/j.jacc.2017.03.571.

P. J. Slomka, “Machine learning for predicting death and heart attacks from CCTA,” Journal of Cardiovascular Computed Tomography, vol. 12, no. 3. Elsevier Inc., pp. 210–211, May 01, 2018. doi: 10.1016/j.jcct.2018.05.008.

Z. I. Attia et al., “An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction,” The Lancet, vol. 394, no. 10201, pp. 861–867, Sep. 2019, doi: 10.1016/S0140-6736(19)31721-0.

K. W. Johnson et al., “Artificial Intelligence in Cardiology,” Journal of the American College of Cardiology, vol. 71, no. 23. Elsevier USA, pp. 2668–2679, Jun. 12, 2018. doi: 10.1016/j.jacc.2018.03.521.

S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, and N. Qureshi, “Can Machine-learning improve cardiovascular risk prediction using routine clinical data?,” PLoS One, vol. 12, no. 4, Apr. 2017, doi: 10.1371/journal.pone.0174944.

J. A. M. N. M. T. A. M. A. T. S. W. G. Azmi, “A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data,” Med Eng Phys, vol. 105, p. 103825, Jul. 2022.

A. Harsh, C. Janki, A. Sarvesh, and G. Yash, “Heart Failure Prediction using Machine Learning with Exploratory Data Analysis,” IEEE, Jun. 2021.

R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 130–139. doi: 10.1016/j.procs.2020.06.017.

N. Kagiyama, S. Shrestha, P. D. Farjo, and P. P. Sengupta, “Artificial intelligence: practical primer for clinical research in cardiovascular disease,” Journal of the American Heart Association, vol. 8, no. 17. American Heart Association Inc., Sep. 03, 2019. doi: 10.1161/JAHA.119.012788.

C. Krittanawong et al., “Machine learning prediction in cardiovascular diseases: a meta-analysis,” Sci Rep, vol. 10, no. 1, Dec. 2020, doi: 10.1038/s41598-020-72685-1.

N. Komal Kumar, G.Sarika Sindhu, D.Krishna Prashanthi, and A.Shaeen Sulthana, Analysis and Prediction of Cardio Vascular Disease using Machine Learning Classifiers. 2020.

T. R. Mahesh et al., “AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/9005278.

A. Al Ahdal et al., “Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning,” J Healthc Eng, vol. 2023, 2023, doi: 10.1155/2023/9738123.

H. Jindal, S. Agrawal, R. Khera, R. Jain, and P. Nagrath, “Heart disease prediction using machine learning algorithms,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Jan. 2021. doi: 10.1088/1757-899X/1022/1/012072.

Bangladesh. F. of E. and E. E. Khulna University of Engineering & Technology, Institute of Electrical and Electronics Engineers. Bangladesh Section, and Institute of Electrical and Electronics Engineers, 2019 4th International Conference on Electrical Information and Communication Technology (EICT).

“Heart Disease Dataset.” [Online]. Available: https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

F. Saleh Alotaibi, “Implementation of Machine Learning Model to Predict Heart Failure Disease,” 2019. [Online]. Available: www.ijacsa.thesai.org

“Azure Machine Learning Documentation - Data preprocessing,” https://learn.microsoft.com/en-us/azure/architecture/data-science-process/prepare-data.

“Azure Machine Learning Documentation - Team Data Science Process,” https://learn.microsoft.com/en-us/azure/architecture/data-science-process/overview.

A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, R. Sun, and I. Garciá-Magarinõ, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mobile Information Systems, vol. 2018, 2018, doi: 10.1155/2018/3860146.

J. Barnes, Azure Machine Learning Microsoft Azure Essentials. Microsoft Press, 2015.

S. Rajagopal, K. S. Hareesha, and P. P. Kundapur, “Performance analysis of binary and multiclass models using azure machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 978–986, 2020, doi: 10.11591/ijece.v10i1.pp978-986.

N. I. M. Y. S. A. M. A. N. H. I. M. A. H. and A. S. B. A. A. Milad, “Using an azure machine learning approach for flexible pavement maintenance,” 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pp. 146–150, 2020.

B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/5594899.

C. M. Bishop, Neural networks for pattern recognition. Oxford University Press, Inc.198 Madison Ave. New York, NYUnited States, 1996.

B. Ekaba, Logistic regression. Building machine learning and deep learning models on google cloud platform," A comprehensive guide for beginners. 2019.