Classification of Death Related to Heart Failure by Machine Learning Algorithms

Year 2021, Volume: 1 Issue: 1, 13 - 18, 15.01.2021

Remzi Gürfidan , Mevlüt Ersoy

Abstract

The increase in the number of individuals with heart diseases and deaths associated with these diseases tops the list of causes of death. Early detection and treatment can reduce the risk of death of candidates with heart disease and people with heart disease. With the expansion of artificial intelligence technology in the field of health in recent years, artificial intelligence models with prediction and classification capability that will contribute positively to patients and health workers are being developed.

In this study, the heart disease mortality status was classified according to the clinical data and life information of the patients included in the heart failure data set. The aim of this study is to evaluate the mortality associated with heart disease based on the clinical data and life information of the patients and to guide patients and doctors to early diagnosis or early treatment methods. Classification processes were performed with different machine learning algorithms and success rates were shown. Different algorithms have been tested to achieve success rates between 73% and 83%. Among the tried algorithms, the most successful classification process is provided by the Support Vector Machine (SVM) algorithm.

Keywords

Machine Learning, Healthcare, Hearth Failure, Support Vector Machine

References

• Türkiye Cumhuriyeti Sağlık Bakanlığı Sağlık İstatistikleri Yıllığı, 2015
• Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020).
• Ahmad, T., Munir, A., Bhatti, S. H., Aftab, M., & Raza, M. A. (2017). Survival analysis of heart failure patients: A case study. PloS one, 12(7), e0181001.
• Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC medical informatics and decision making, 20(1), 16.
• Zemmal, N., Azizi, N., Sellami, M., Cheriguene, S., Ziani, A., AlDwairi, M., & Dendani, N. (2020). Particle Swarm Optimization Based Swarm Intelligence for Active Learning Improvement: Application on Medical Data Classification. Cognitive Computation, 1-20.
• Hsu, C. N., Liu, C. L., Tain, Y. L., Kuo, C. Y., & Lin, Y. C. (2020). Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study. Journal of Medical Internet Research, 22(8), e16903.
• Al Shibli, M. (2020). Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology (AI-MLBBC): Anti-Crime-Theft Smart Wall Defense. In Encyclopedia of Criminal Activities and the Deep Web (pp. 1089-1111). IGI Global.
• Jiang, Y., Bao, X., Hao, S., Zhao, H., Li, X., & Wu, X. (2020). Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction. Water Resources Management, 1-17.
• He, L., Chen, S., Liang, Y., Hou, M., & Chen, J. (2020). Infilling the missing values of groundwater level using time and space series: case of Nantong City, east coast of China. Earth Science Informatics, 1-15.
• Yin, H. (2020). Smart Healthcare Via Efficient Machine Learning (Doctoral dissertation, Princeton University).
• Anand, H., Anand, A., Das, I., Rautaray, S. S., & Pandey, M. (2020, July). Hridaya Kalp: A Prototype for Second Generation Chronic Heart Disease Detection and Classification. In International Conference on Innovative Computing and Communications (pp. 321-329). Springer, Singapore.
• Pouriyeh, S., Vahid, S., Sannino, G., De Pietro, G., Arabnia, H., & Gutierrez, J. (2017, July). A comprehensive investigation and comparison of Machine Learning Techniques in the domain of heart disease. In 2017 IEEE Symposium on Computers and Communications (ISCC) (pp. 204-207). IEEE.
• Buettner, R., & Schunter, M. (2019, October). Efficient machine learning based detection of heart disease. In 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom) (pp. 1-6). IEEE.
• Parthiban, G., & Srivatsa, S. K. (2012). Applying machine learning methods in diagnosing heart disease for diabetic patients. International Journal of Applied Information Systems (IJAIS), 3(7), 25-30.
• Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554.
• Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018). A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018
• Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., & Fettich, J. (1999). Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial intelligence in medicine, 16(1), 25-50.