Mortality prediction for acute decompensated heart failure patient using fuzzy neural network
DOI:
https://doi.org/10.11113/mjfas.v16n4.1808Keywords:
mortality prediction, artificial neural network, fuzzy neural network, acute decompensated heart failureAbstract
It has been reported that patients admitted with acute decompensated heart failure (ADHF) face high risk of mortality where 30-day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of fuzzy neural network to predict mortality for the patient admitted with ADHF. Results show that fuzzy neural network can predict mortality for ADHF patient with good prediction accuracy with overall accuracy of 88.8% for partition 50 and 90.40% for partition 80.
References
Abu Yazid, M. H., Talib, S., Satria, M. H. 2018. Artificial neural network parameter tuning framework for heart disease classification. Proceeding of the Electrical Engineering Computer Science and Informatics, 5: 674-679.
Anooj, P. K. 2012. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University-Computer and Information Sciences 24(1): 27-40.
Blake, C. L., Merz, C. J. UCI Repository of machine learning databases. Irvine, C. A. 1998.
http://www.ics.uci.edu/~mlearn/MLRepository.html
Das, R., Turkoglu, I., Sengur, A. 2009. Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications, 36(4):7675–7680.
Devaraj, S. 2015. Drugs & Diseases, Laboratories Medicine, Albumin. Available from: https://emedicine.medscape.com/article/2054430-overview>.
El-Bialy, R., Salamay, M. A., Karam, O. H., Khalifa, M. E. 2015. Feature analysis of coronary artery heart disease data sets. Procedia Computer Science, 65:459–468.
Hedeshi, N., Ghadiri, Abadeh. M. S. 2014. Coronary artery disease detection using a fuzzy-boosting PSO approach. Computational Intelligence and Neuroscience, 6: 1-12.
Shao, Y. E., Hou, C.-D., Chiu, C.-C. 2014. Hybrid intelligent modeling schemes for heart disease classification. Applied Soft Computing Journal, 14: 47–52.
Nahato, Biredagn, N., Harichandran, K. N., Arputharaj, K. 2015. Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computational and Mathematical Methods in Medicine.
Nowicki, Robert K., Janusz, T. Starczewski. 2017. A new method for classification of imprecise data using fuzzy rough fuzzification. Information Sciences, 414: 33-52.
Paolo, G. Three-way analysis of imprecise data. 2010. Journal of Multivariate Analysis, 101(3), 568-582.
Polat, K., Sahan, S., Kodaz, H., Günes, S. 2005. A new classification method to diagnosis heart disease: Supervised artificial immune system (AIRS). Pro- ceedings of the Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN).
Remzi, M., Anagnostou, T., Ravery, V., Zlotta, A., Stephan, C., Marberger, M., Djavan, B. 2003. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology, Sep; 62(3):456-460.
Sagir, Masanawa, A., Sathasivam, S. 2017. A novel adaptive neuro fuzzy inference system based classification model for heart disease prediction. Pertanika Journal of Science & Technology 25(1).
Vijaya, K., Nehemiah, H. K., Kannan, A, Bhuvaneswari, N.G. 2010. Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases. International Journal of Data Mining, Modelling and Management, 2(4): 388-402.
Xu, Y., Pan, X., Zhou, Z., Yang, Z., Zhang, Y. 2015. Structural least square twin support vector machine for classification. Applied Intelligence, 42(3):527–536.
Zain, Mohd, A., Haron, H., Sharif, S. 2010. Prediction of surface roughness in the end milling machining using Artificial Neural Network. Expert Systems with Applications 37(2): 1755-1768.
Zhang, Guoqiang, B., Patuwo, E., Michael, Y. H. 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14(1): 35-62.
