Abstract
Nowadays, population is growing on large scale along with the problems faced by the people are also increasing. Thus, healthcare industry is making lot of technological advancements to provide effective, faster and cheaper treatment to people. Digitization of health records are also expanding in zettabytes. Electronic Health Record (EHR) containing all the patient’s medical history, demographics and other clinical data is also used in hospitals for improved care co-ordination. To avoid critical conditions of people from chronic diseases like hypertension, diabetes, hyperlipidemia etc. there is a need for building a health risk prediction model. But, when whole EHR data is provided to this risk prediction model causes overfitting of features. Overfitting is caused when model learns the details & noise from dataset, thus having negative impact on the performance. Hence, a feature selection approach is proposed for discarding redundant features from EHR. Improved sparse logistic regression method selects the best suitable parameters and forwards to risk prediction model. This regression method improvises the model with the use of logistic loss function that controls the sparsity factor. Neural network is used as a risk prediction model. This paper describes the risk prediction of hypertension disease. Thus, people could take preventive measures for the disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Scheurwegs, G.E., Cule, B.: Selecting relevant features from electronic health record for clinical code prediction. J. Bioinform. 74, 92–103 (2017)
Sze, V., Chen, Y.-H.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017)
Abramovich, F., Grinshtein, V.: High dimensional classification by sparse logistic regression. Bioinformatics 34, 485–493 (2018)
Zamuda, A., Zarges, C., Stiglic, G.: Stability selection using genetic algorithm and logistis linear regression on healthcare records. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 143–144 (2017)
Kollias, D., Tagaris, A.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4, 119–131 (2018)
Zhao, J., Asker, L., Bostrom, H.: Learning from heterogeneous temporal data in electronic health records. J. Biomed. Informat. (2016). https://doi.org/10.1016/j.jbi.2016.11.006
Koutsoukas, A., Monaghan, K.J., Li, X., Huan, J.: Deeplearning: investigating deep neural networks hyperparameters and comparison of performance to shallow methods for modeling bioactivity data. J. Cheminformat. (2017). https://doi.org/10.1186/s13321-017-0226-y
Pham, T., Tran, T.: DeepCare: a deep dynamic memory model for predictive medicine. In: PAKDD 2016: Advances in Knowledge Discovery and Data Mining, pp. 30–41. Springer, Cham (2016)
Martin, K., Farhana, Z., Barber, D.: Using machine learning to predict hypertension from a clinical dataset. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), December 2016
Nezhada, M., Zhu, D.: SAFS: a deep feature selection approach for precision medicine. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Choi, E., Searles, E.: Multilayer representation learning for medical concepts. In: KDD 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1495–1504, August 2016
Zhao, J., Asker, L., Bostrom, H.: Learning from heterogeneous temporal data in electronic health records. J. Biomed. Inform. (2016). https://doi.org/10.1016/j.jbi.2016.11.006
Nguyen, P., Tran, T., Wickramasinghe, N.: Deepr: a convolutional net for medical records. IEEE J. Biomed. Health Inform. (2016). https://doi.org/10.1109/jbhi.2633963
Hira, Z.M., Gillies, D.F.: A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform. 10, 13 (2015)
Zhou, J., Wang, F.: From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records. In: KDD 2014 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2014)
Wang, F., Zhang, P.: Clinical risk prediction with multilinear sparse logistic regression. In: KDD 2014 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145–154 (2014)
Zhou, C., Jia, Y., Motani, M.: Learning deep representations from heterogeneous patient data for predictive diagnosis. In: Clinical Databases and Information Systems, pp. 115–123. ACM, August 2017
Qiu, M., Song, Y., Akagi, F.: Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market. Chaos Solitons Fractals 85, 1–7 (2016). Nonlinear Science, and Non equilibrium and Complex Phenomena
Li, H., Li, X., Jia, X., Ramanathan, M.: Bone disease prediction and phenotype discovery using feature representation over electronic health records. In: BCB 2015 Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 212–221. ACM (2015)
Yao, C., Qu, Y., Jin, B.: A convolutional neural network model for online medical guidance, vol. 4, pp. 4094–4103. IEEE (2016)
Sideris, C., Alshurafa, N.: A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2534–2537. IEEE (2015)
Shickel, B., Tighe, P.J., Bihorac, A.: Deep EHR: a survey of recent advances in deep learning techniques for Electronic Health Record (EHR) analysis. IEEE J. Biomed. Health Inform., 2168–2194 (2017)
Zhao, R., Yan, R., Chen, Z.: Deep learning and its applications to machine health monitoring: a survey. J. Latex Class Files 14, 1–14 (2016)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Choi, E., Bahadori, M.T.: Doctor AI: predicting clinical events via recurrent neural networks. Proc. Mach. Learn. Res. 56 (2016)
Che, Z., Cheng, Y., Sun, Z.: Exploiting convolutional neural network for risk prediction with medical feature embedding. In: NIPS 2016 Workshop on Machine Learning for Health (ML4HC), Cornell University Library (2017)
Zhong, J., Wang, J.: A feature selection method for prediction essential protein. Tsinghua Sci. Technol. 20, 491–499 (2015)
Canino, G., Suo, Q., Guzzi, P.H.: Feature selection model for diagnosis, electronic medical records and geographical data correlation. In: BCB 2016 Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 616–621. ACM (2016)
Lee, B.J., Kim, J.Y.: Identification of Type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J. Biomed. Health Inform., 2168–2194 (2015)
Pal, M.: Multinomial logistic regression-based feature selection for hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 14, 214–220 (2012)
Grosan, C., Abraham, A.: Intelligent Systems: A Modern Approach. Intelligent Systems Reference Library Series, 450 p. Springer, Heidelberg (2011). ISBN 978-3-642-21003-7
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
Gajare, S., Sonawani, S. (2020). Improved Logistic Regression Approach in Feature Selection for EHR. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-16657-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16656-4
Online ISBN: 978-3-030-16657-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)