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Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics

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Abstract

Recent real medical datasets show that the number of outpatients in China has sharply increased since 2013, when the Chinese health insurance reform started. This situation leads to increased waiting time for the outpatients; in particular, the normal operation of a hospital will be congested at rush hour. The existence of this problem in outpatient departments causes a reduction in doctors’ diagnostic time, and a high working strength is required to address this issue. In this paper, a simultaneous model based on machine learning is proposed for aiding outpatient doctors in performing diagnoses. We use Support Vector Machine (SVM) and Neural Networks (NN) to classify hyperlipemia using the clinical features extracted from a real medical dataset. The results, with an accuracy of 90 %, indicate that our Simultaneously Aided Diagnosis Model (SADM) applied to aid diagnosis for outpatient doctors and achieves the objective of increasing efficiency and reducing working strength.

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References

  1. Bates D W, Saria S, Ohno-Machado L, et al. (2014) Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff 33 (7):1123–1131

    Article  Google Scholar 

  2. Bellazzi R, Zupan B (2008) Predictive data mining in clinical medicine: Current issues and guidelines. Int J Med Inform 77:81–97

    Article  Google Scholar 

  3. Bron E E, Smits M, et al. (2015) Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE Journal of Biomedical and Health Informatics 19(5):1617–1626

    Article  Google Scholar 

  4. Chang C-D, Wang C-C, Jiang B C (2011) Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Syst Appl 38:5507– 5513

    Article  Google Scholar 

  5. Chen M (2014) NDNC-BAN: Supporting Rich Media Healthcare Services Via Named Data Networking in Cloud-assisted Wireless Body Area Networks. Inf Sci 284 (10):142–156

    Article  Google Scholar 

  6. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Networks and Applications 19(2):171– 209

    Article  Google Scholar 

  7. Chen M, Mao S, Zhang Y, Leung V (2014) Big Data: Related Technologies, Challenges and Future Prospects, SpringerBriefs in Computer Science, Springer ISBN 978-3-319-06245-7

  8. Chen M, Wan J, Gonzalez S, Liao X, Leung V (2014) A survey of recent developments in home M2M networks. IEEE Commun Surv Tutorials 16(1):98–114

    Article  Google Scholar 

  9. Chen M, Ma Y, Song J, Lai C, Hu B (2016) Smart Clothing: Connecting human with clouds and big data for sustainable health monitoring mobile networks and applications

  10. Çnara M, Enginb M, Enginb E Z, Ziya Ateşçia Y (2009) Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Syst Appl 36:6357– 6361

    Article  Google Scholar 

  11. Chu S-M, Shih W-T, Yang Y-H, Chen P-C, Chu Y-H (2015) Use of traditional Chinese medicine in patients with hyperlipidemia: Apopulation-based study in Taiwan. J Ethnopharmacol 168:129– 135

    Article  Google Scholar 

  12. Dogan S, Turkoglu I (2008) Diagnosing hyperlipidemia using association rules. Mathematical and Computational Applications 13(3):193–202

    Article  Google Scholar 

  13. Esfandiari N, Babavalian M R, Moghadam A-M E, Tabar V K (2014) Knowledge discovery in medicine: Current issue and future trend. Expert Syst Appl 41:4434–4463

    Article  Google Scholar 

  14. Ge X, Tu S, Han T, Li Q, Mao G (2015) Energy Efficiency of Small Cell Backhaul Networks Based on Gauss-Markov Mobile Models. IET Networks 4(2):158–167

    Article  Google Scholar 

  15. Ge X, Yang B, Ye J, Mao G, Wang C-X, Han T (2015) Spatial Spectrum and Energy Efficiency of Random Cellular Networks. IEEE Trans Commun 63(3):1019–1030

    Article  Google Scholar 

  16. Lin K, Chen M, Deng J, Hassan M, Fortino G (2016) Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings, IEEE Transactions on Automation Science and Engineering

  17. Liu C, et al. (2011) Efficient network management for context-aware participatory sensing. SECON’11:116–124

  18. Liu C, et al. (2013) Sketching the data center network traffic. IEEE Netw 27 (4):33–39

    Article  Google Scholar 

  19. Liu C, et al. (2014) Efficient naming, addressing and profile services in Internet-of-Things sensory environments. Ad Hoc Netw 18:85–101

    Article  Google Scholar 

  20. Liu C, et al. (2014) Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing 2(4):473–487

    Article  Google Scholar 

  21. Liu C, et al. (2014) A generic Admission-Control methodology for packet networks. IEEE Trans Wirel Commun 13(2):604–617

    Article  Google Scholar 

  22. Liu C, et al. (2015) Energy-Aware Participant selection for Smartphone-Enabled mobile crowd sensing. IEEE Syst J 99:1–12

    Google Scholar 

  23. Liu C, et al. (2015) Toward QoI and Energy Efficiency in Participatory Crowdsourcing. IEEE Trans Veh Technol 64(10):4684–4700

    Article  Google Scholar 

  24. Liu C, et al. (2016) Energy-Efficient Event detection by participatory sensing under budget constraints. IEEE Syst J 99:1–12

    Google Scholar 

  25. Nori N, Kashima H, Yamashita K, et al. (2015) Simultaneous Modeling of multiple diseases for mortality prediction in acute hospital care ssACM SIGKDD conference on knowledge discovery and data mining, (KDD’15), sydney, NSW, Australia, 13-15

  26. Premarathne U S, Fengling H, et al. (2013) Preference based load balancing as an outpatient appointment scheduling aid, 35th Annual international Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3-7

  27. Shamim Hossain M (2015) Cloud-supported Cyber-Physical Framework for Patients Monitoring, IEEE Systems J

  28. Shamim Hossain M, Muhammad G (2016) Cloud-assisted Industrial Internet of Things (IIot)- enabled framework for Health Monitoring. Elsevier Computer Networks 101(2016):192– 202

    Article  Google Scholar 

  29. Shamim Hossain M, Muhammad G, Al Hamid M F, Song B (2016) Audio-Visual Emotion-aware Big Data Recognition towards 5G Springer Mobile Networks and Applications

  30. Sheng Z, et al. (2015) Energy-efficient relay selection for cooperative relaying in wireless multimedia networks. IEEE Trans Veh Technol 64(3):1156–1170

    Article  Google Scholar 

  31. Yurur O, et al. (2014) A survey of context-aware middleware designs for human activity recognition. IEEE Commun Mag 52(6):24–31

    Article  Google Scholar 

  32. Zhang B, et al. (2015) An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints. ACM Trans Intell Syst Technol 6 (3):42

    Google Scholar 

  33. Zhang B, et al. (2016) Privacy-preserving QoI-Aware Participant Coordination for Mobile Crowdsourcing. Comput Netw 101:29–41

    Article  Google Scholar 

  34. Zhang B, et al. (2016) Energy-Efficient Software-Defined Data collection by participatory sensing. IEEE Sensors Journal 99:1–1

    Google Scholar 

  35. Zhang Y, Chen M, Mao S, Hu L, Leung V (2014) CAP: Crowd Activity prediction based on big data analysis. IEEE Netw 28(4):52–57

    Article  Google Scholar 

  36. Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning, PeerJ. pp.1251-1280

    Article  Google Scholar 

  37. Zhang Y, Wang S, et al. (2015) Magnetic Resonance Brain Image Classi?cation Basedon Weighted-Type Fractional Fourier Transform andNonparallel Support Vector Machine. Int J Imaging Syst Technol 25:317–327

    Article  Google Scholar 

  38. Zhang Y, Wang S, et al. (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med Mater Eng 26:1283–1290

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to extend their sincere appreciations to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for its funding of this research through the Profile Research Group project (PRG-1436-17).

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Correspondence to Kui Duan.

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Hu, Y., Duan, K., Zhang, Y. et al. Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics. Multimed Tools Appl 77, 3729–3743 (2018). https://doi.org/10.1007/s11042-016-3719-1

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  • DOI: https://doi.org/10.1007/s11042-016-3719-1

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