Improved Logistic Regression Approach in Feature Selection for EHR

  • Shreyal GajareEmail author
  • Shilpa Sonawani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)


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.


Electronic Health Record (EHR) Feature selection Logistic regression Overfitting Neural networks 


  1. 1.
    Scheurwegs, G.E., Cule, B.: Selecting relevant features from electronic health record for clinical code prediction. J. Bioinform. 74, 92–103 (2017)Google Scholar
  2. 2.
    Sze, V., Chen, Y.-H.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017)CrossRefGoogle Scholar
  3. 3.
    Abramovich, F., Grinshtein, V.: High dimensional classification by sparse logistic regression. Bioinformatics 34, 485–493 (2018)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Kollias, D., Tagaris, A.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4, 119–131 (2018)CrossRefGoogle Scholar
  6. 6.
    Zhao, J., Asker, L., Bostrom, H.: Learning from heterogeneous temporal data in electronic health records. J. Biomed. Informat. (2016). Scholar
  7. 7.
    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).
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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 2016Google Scholar
  10. 10.
    Nezhada, M., Zhu, D.: SAFS: a deep feature selection approach for precision medicine. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)Google Scholar
  11. 11.
    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 2016Google Scholar
  12. 12.
    Zhao, J., Asker, L., Bostrom, H.: Learning from heterogeneous temporal data in electronic health records. J. Biomed. Inform. (2016). Scholar
  13. 13.
    Nguyen, P., Tran, T., Wickramasinghe, N.: Deepr: a convolutional net for medical records. IEEE J. Biomed. Health Inform. (2016).
  14. 14.
    Hira, Z.M., Gillies, D.F.: A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform. 10, 13 (2015)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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 2017Google Scholar
  18. 18.
    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 PhenomenaMathSciNetCrossRefGoogle Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Yao, C., Qu, Y., Jin, B.: A convolutional neural network model for online medical guidance, vol. 4, pp. 4094–4103. IEEE (2016)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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)Google Scholar
  24. 24.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  25. 25.
    Choi, E., Bahadori, M.T.: Doctor AI: predicting clinical events via recurrent neural networks. Proc. Mach. Learn. Res. 56 (2016)Google Scholar
  26. 26.
    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)Google Scholar
  27. 27.
    Zhong, J., Wang, J.: A feature selection method for prediction essential protein. Tsinghua Sci. Technol. 20, 491–499 (2015)MathSciNetCrossRefGoogle Scholar
  28. 28.
    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)Google Scholar
  29. 29.
    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)Google Scholar
  30. 30.
    Pal, M.: Multinomial logistic regression-based feature selection for hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 14, 214–220 (2012)CrossRefGoogle Scholar
  31. 31.
    Grosan, C., Abraham, A.: Intelligent Systems: A Modern Approach. Intelligent Systems Reference Library Series, 450 p. Springer, Heidelberg (2011). ISBN 978-3-642-21003-7CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Maharashtra Institute of TechnologyPuneIndia

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