Prediction of Survival of Heart Failure Patients: An Application of Classification-Based Machine Learning Algorithms
Year 2023,
, 362 - 369, 03.05.2023
Sinem Bozkurt Keser
,
Kemal Keskin
Abstract
Cardio-vascular diseases are among the diseases that cause the most deaths worldwide. Heart failure,
a cardiovascular disease, is a condition in which the heart cannot pump the blood that the body needs.
Deaths occur as a result of this disease, which is frequently seen in our country. In this study, a machine
learning-based approach is proposed to predict survival or death of patients with heart failure. The
effectiveness of the proposed method is evaluated using three different classification algorithms. In the
experiments performed, the highest accuracy values (86.67%) was achieved with the Artificial Neural
Network algorithm. The proposed method will guide the preparation of more effective and appropriate
treatment plans for heart failure patients with a high risk of death.
References
- Aktaş Potur, E., ve Erginel, N. (2021). Kalp Yetmezliği Hastalarının Sağ Kalımlarının Sınıflandırma Algoritmaları ile Tahmin Edilmesi. European Journal of Science and Technology, 24, 112–118.
- Angraal, S., Mortazavi, B. J., Gupta, A., Khera, R., Ahmad, T., Desai, N. R., Jacoby, D. L., Masoudi, F. A., Spertus, J. A., and Krumholz, H. M. (2020). Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction. JACC: Heart Failure, 8(1), 12–21.
- Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F. M., Chow, B. J., and Dwivedi, G. (2019). Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death. PLOS ONE, 14(6), e0218760.
- Aydın, A. (2021). Kalp Yetmezliği Hastalarında Kritik Parametre Seçimi ve Sağkalım Modeli Geliştirilmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 155–162.
- Bergstra, J., and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2).
Breiman, L. (2001). Random Forest. Machine Learning, 45(1), 5–32.
- Buchan, T. A., Ross, H. J., McDonald, M., Billia, F., Delgado, D., Duero Posada, J. G., Luk, A., Guyatt, G. H., and Alba, A. C. (2019). Physician Prediction versus Model Predicted Prognosis in Ambulatory Patients with Heart Failure. The Journal of Heart and Lung Transplantation, 38(4), S381.
- Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. (1998). Discovering Data Mining. From Concept to Implementation. Prentice Hall.
- Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357.
- Chen, T., and Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
- Chicco, D., and 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.
- Degertekin, M., Erol, C., Ergene, O., Tokgozoglu, L., Aksoy, M., Erol, M. K., Eren, M., Sahin, M., Eroglu, E., Mutlu, B., ve Kozan, O. (2012). Heart fAilure Prevalence and Predictors in TurkeY (HAPPY) Çalışması. Turk Kardiyoloji Dernegi Arsivi-Archives of the Turkish Society of Cardiology, 40(4), 298–308.
- Dünya Sağlık Örgütü (DSÖ). (2021, June 25). Cardiovascular-diseases. Https://Www.Who.Int/Health-Topics/Cardiovascular-Diseases/.
- Erdas, C. B., and Olcer, D. (2020). A Machine Learning-Based Approach to Detect Survival of Heart Failure Patients. 2020 Medical Technologies Congress (TIPTEKNO), 1–4.
- Gu, J., Pan, J., Lin, H., Zhang, J., and Wang, C. (2021). Characteristics, prognosis and treatment response in distinct phenogroups of heart failure with preserved ejection fraction. International Journal of Cardiology, 323, 148–154.
- Han, J., and Kamber, M. (2001). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann Publishers.
- Harrington, P. (2012). Machine Learning in Action. Manning Publications.
- Haykin, S. S. (1999). Neural Networks: A comprehensive Foundation. In Prentice-Hall, Inc (Vol. 7458). Prentice Hall.
- Hedman, Å. K., Hage, C., Sharma, A., Brosnan, M. J., Buckbinder, L., Gan, L.-M., Shah, S. J., Linde, C. M., Donal, E., Daubert, J.-C., Mälarstig, A., Ziemek, D., and Lund, L. (2020). Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Heart, 106(5), 342–349.
- Le, M. T., Thanh Vo, M., Mai, L., and Dao, S. V. . (2020). Predicting heart failure using deep neural network. 2020 International Conference on Advanced Technologies for Communications (ATC), 221–225.
- Meng, F., Zhang, Z., Hou, X., Qian, Z., Wang, Y., Chen, Y., Wang, Y., Zhou, Y., Chen, Z., Zhang, X., Yang, J., Zhang, J., Guo, J., Li, K., Chen, L., Zhuang, R., Jiang, H., Zhou, W., Tang, S., … Zou, J. (2019). Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China. BMJ Open, 9(5), e023724.
- Mitchell, T. (1997). Machine Learning. McGraw Hill.
- Moreno-Sanchez, P. A. (2020). Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees. 2020 IEEE International Conference on Big Data (Big Data), 4902–4910.
- Segar, M. W., Patel, K. V., Ayers, C., Basit, M., Tang, W. H. W., Willett, D., Berry, J., Grodin, J. L., and Pandey, A. (2020). Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis. European Journal of Heart Failure, 22(1), 148–158.
- Wilstup, C., and Cave, C. (2021). Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths. MedRxiv, 2021.01.15.21249874.
Kalp Yetmezliği Hastalarının Sağ Kalım Tahmini: Sınıflandırmaya Dayalı Makine Öğrenmesi Algoritmalarının Bir Uygulaması
Year 2023,
, 362 - 369, 03.05.2023
Sinem Bozkurt Keser
,
Kemal Keskin
Abstract
Kardiyo-vasküler hastalıklar dünya genelinde en çok ölüme sebep olan hastalıklar arasında yer
almaktadır. Bir kardiyo-vasküler hastalık olan kalp yetmezliği, kalbin vücudun ihtiyaç duyduğu kanı
pompalayamaması durumudur. Ülkemizde sıklıkla görülen bu hastalığın sonucu olarak ölümler
yaşanmaktadır. Bu çalışmada kalp yetmezliğe sahip hastaların sağ kalım veya ölüm durumlarının tahmin
edilmesi için makine öğrenmesi tabanlı bir yaklaşım önerilmektedir. Üç farklı sınıflandırma algoritması
kullanılarak önerilen yöntemin etkinliği değerlendirilmektedir. Gerçekleştirilen deneylerde, Yapay Sinir
Ağı algoritması ile en yüksek doğruluk değerine (86.67%) ulaşılmıştır. Önerilen yöntem, ölüm riskinin
yüksek olduğu kalp yetmezliği hastalarına daha etkin ve uygun tedavi planlarının hazırlanması açısından
yol gösterici olacaktır.
References
- Aktaş Potur, E., ve Erginel, N. (2021). Kalp Yetmezliği Hastalarının Sağ Kalımlarının Sınıflandırma Algoritmaları ile Tahmin Edilmesi. European Journal of Science and Technology, 24, 112–118.
- Angraal, S., Mortazavi, B. J., Gupta, A., Khera, R., Ahmad, T., Desai, N. R., Jacoby, D. L., Masoudi, F. A., Spertus, J. A., and Krumholz, H. M. (2020). Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction. JACC: Heart Failure, 8(1), 12–21.
- Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F. M., Chow, B. J., and Dwivedi, G. (2019). Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death. PLOS ONE, 14(6), e0218760.
- Aydın, A. (2021). Kalp Yetmezliği Hastalarında Kritik Parametre Seçimi ve Sağkalım Modeli Geliştirilmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 155–162.
- Bergstra, J., and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2).
Breiman, L. (2001). Random Forest. Machine Learning, 45(1), 5–32.
- Buchan, T. A., Ross, H. J., McDonald, M., Billia, F., Delgado, D., Duero Posada, J. G., Luk, A., Guyatt, G. H., and Alba, A. C. (2019). Physician Prediction versus Model Predicted Prognosis in Ambulatory Patients with Heart Failure. The Journal of Heart and Lung Transplantation, 38(4), S381.
- Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. (1998). Discovering Data Mining. From Concept to Implementation. Prentice Hall.
- Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357.
- Chen, T., and Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
- Chicco, D., and 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.
- Degertekin, M., Erol, C., Ergene, O., Tokgozoglu, L., Aksoy, M., Erol, M. K., Eren, M., Sahin, M., Eroglu, E., Mutlu, B., ve Kozan, O. (2012). Heart fAilure Prevalence and Predictors in TurkeY (HAPPY) Çalışması. Turk Kardiyoloji Dernegi Arsivi-Archives of the Turkish Society of Cardiology, 40(4), 298–308.
- Dünya Sağlık Örgütü (DSÖ). (2021, June 25). Cardiovascular-diseases. Https://Www.Who.Int/Health-Topics/Cardiovascular-Diseases/.
- Erdas, C. B., and Olcer, D. (2020). A Machine Learning-Based Approach to Detect Survival of Heart Failure Patients. 2020 Medical Technologies Congress (TIPTEKNO), 1–4.
- Gu, J., Pan, J., Lin, H., Zhang, J., and Wang, C. (2021). Characteristics, prognosis and treatment response in distinct phenogroups of heart failure with preserved ejection fraction. International Journal of Cardiology, 323, 148–154.
- Han, J., and Kamber, M. (2001). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann Publishers.
- Harrington, P. (2012). Machine Learning in Action. Manning Publications.
- Haykin, S. S. (1999). Neural Networks: A comprehensive Foundation. In Prentice-Hall, Inc (Vol. 7458). Prentice Hall.
- Hedman, Å. K., Hage, C., Sharma, A., Brosnan, M. J., Buckbinder, L., Gan, L.-M., Shah, S. J., Linde, C. M., Donal, E., Daubert, J.-C., Mälarstig, A., Ziemek, D., and Lund, L. (2020). Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Heart, 106(5), 342–349.
- Le, M. T., Thanh Vo, M., Mai, L., and Dao, S. V. . (2020). Predicting heart failure using deep neural network. 2020 International Conference on Advanced Technologies for Communications (ATC), 221–225.
- Meng, F., Zhang, Z., Hou, X., Qian, Z., Wang, Y., Chen, Y., Wang, Y., Zhou, Y., Chen, Z., Zhang, X., Yang, J., Zhang, J., Guo, J., Li, K., Chen, L., Zhuang, R., Jiang, H., Zhou, W., Tang, S., … Zou, J. (2019). Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China. BMJ Open, 9(5), e023724.
- Mitchell, T. (1997). Machine Learning. McGraw Hill.
- Moreno-Sanchez, P. A. (2020). Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees. 2020 IEEE International Conference on Big Data (Big Data), 4902–4910.
- Segar, M. W., Patel, K. V., Ayers, C., Basit, M., Tang, W. H. W., Willett, D., Berry, J., Grodin, J. L., and Pandey, A. (2020). Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis. European Journal of Heart Failure, 22(1), 148–158.
- Wilstup, C., and Cave, C. (2021). Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths. MedRxiv, 2021.01.15.21249874.