Abstract
The burden of cardiovascular diseases is increasing, and the annual growth rate of hospitalization expenses for cardiovascular diseases is much higher than that of GDP. Therefore, researchers have developed a number of intelligent systems to predict hospitalization costs for cardiovascular disease. However, there are some problems with these methods, such as the performance of real world data sets and the differences between the feature selection and the actual selection of doctors. This paper proposes a method to construct a Medical Concept Knowledge Graph (MCKG) by combining open source knowledge graphs such as Wikidata and OpenKG, open source knowledge bases such as UMLS, and doctors’ prior medical knowledge. A Medical Instance Knowledge Graph (MIKG) is constructed based on MCKG and the data of cardiovascular disease related medical records from the cooperative hospital. We conduct feature selection according to MIKG, draw feature alternatives, and combine with doctor-defined rules to arrive at final feature selection. We predict hospitalization costs with random forest algorithm. Experimental results show that the average error rate of our method is lower than that of the baseline algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chinese cardiovascular disease report compilation group: Summary of Chinese cardiovascular disease report 2016. China Circul. J. 032, 521–530 (2017)
Zhang, Y., Wang, S.N., Liu, Y.: Application of ARIMA model on predicting monthly hospital admissions and hospitalization expenses for respiratory diseases. China Health statistics 032, 197–200 (2015)
Guyon, I.: An introduction to variable and feature selection. JMLR.org (2003)
Guo, K.W., Pan, H.L., Hou, A.: Classification algorithm based on feature selection and clustering. J. Jilin Univ. (Science Ed.) 056, 395–398 (2018)
Ansong, S., Eteffa, Kalkidan F., Li, C., Sheng, M., Zhang, Y., Xing, C.: How to empower disease diagnosis in a medical education system using knowledge graph. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 518–523. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_52
Sheng, M., Hu, Q., Zhang, Y., Xing, C., Zhang, T.: A data-intensive CDSS platform based on knowledge graph. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, Wei (eds.) HIS 2018. LNCS, vol. 11148, pp. 146–155. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01078-2_13
Xu, Z.L., He, L.R, Wang, Y.F.: Overview of knowledge graph technology. J. Electr. Sci. Technol. 589–606
Research on current situation and strategy of artificial intelligence-assisted diagnosis and treatment. Chinese Eng. Sci. 20, 1–128 (2018)
Sheng, M., et al.: CLMed: a cross-lingual knowledge graph framework for cardiovascular diseases. Web Inf. Syst. Appl. 512–517 (2019)
Uyar, K., lhan, A.: Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Comput. Sci. 120, 588–593 (2017)
Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 1 (2019)
Alarsan, F.I., Younes, M.: Analysis and classification of heart diseases using heartbeat features and machine learning algorithms (2019)
Ali, L., Rahman, A., Khan, A., Zhou, M., Javeed, A., Khan, J.A.: An automated diagnostic system for heart disease prediction based on χ2 statistical model and optimally configured deep neural network. IEEE Access 7, 34938–34945 (2019)
Basciftci, F., Eldem, A.: Using reduced rule base with Expert System for the diagnosis of disease in hypertension. Med. Biol. Eng. Comput. 51, 1287–1293 (2013)
Nahar, J., Imam, T., Tickle, K.S., Chen, Y.-P.P.: Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst. Appl. 40, 96–104 (2013)
Prakash, S., Sangeetha, K., Ramkumar, N.: An optimal criterion feature selection method for prediction and effective analysis of heart disease. Cluster Comput. 22, 11957–11963 (2019)
Gokulnath, C.B., Shantharajah, S.P.: An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Comput. 22, 1–11 (2019)
Zhao, T.T., Yuan, Y.B., Wang, Y.J., Gao, J., He, P.: Heart disease classification based on feature fusion. In: 2017 International Conference on Machine Learning and Cybernetics (2017)
Sarah, P., Ira, K.S., Enzo, F., Matthew, L., Ricardo, G., Ben, G., Daniel, R.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. medical image analysis S1361841518303554 (2018)
Javeed, A., Zhou, S., Yongjian, L., Qasim, I., Noor, A., Nour, R.: An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 7, 180235–180243 (2019)
Singh, Y.K., Sinha, N., Singh, S.K.: Heart disease prediction system using random forest. In: International Conference on Advances in Computing and Data Sciences (2017)
Saunders, C., et al.: Support vector machine. Comput. Sci. 1, 1–28 (2002)
Allison, L.: Coding Ockham’s Razor. Linear Regression, pp. 103–111. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-76433-7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, W., Huang, M., Wu, Q., Cai, H., Sheng, M., Li, X. (2020). Hospitalization Cost Prediction for Cardiovascular Disease by Effective Feature Selection. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-60029-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60028-0
Online ISBN: 978-3-030-60029-7
eBook Packages: Computer ScienceComputer Science (R0)
