Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3729–3743 | Cite as

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

  • Ying Hu
  • Kui DuanEmail author
  • Yin Zhang
  • M. Shamim Hossain
  • Sk Md Mizanur Rahman
  • Abdulhameed Alelaiwi


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.


Machine learning Aided diagnosis SVM Spatio-temporal evolution 



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).


  1. 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):1123–1131CrossRefGoogle Scholar
  2. 2.
    Bellazzi R, Zupan B (2008) Predictive data mining in clinical medicine: Current issues and guidelines. Int J Med Inform 77:81–97CrossRefGoogle Scholar
  3. 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):1617–1626CrossRefGoogle Scholar
  4. 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– 5513CrossRefGoogle Scholar
  5. 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):142–156CrossRefGoogle Scholar
  6. 6.
    Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Networks and Applications 19(2):171– 209CrossRefGoogle Scholar
  7. 7.
    Chen M, Mao S, Zhang Y, Leung V (2014) Big Data: Related Technologies, Challenges and Future Prospects, SpringerBriefs in Computer Science, Springer ISBN 978-3-319-06245-7Google Scholar
  8. 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):98–114CrossRefGoogle Scholar
  9. 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 applicationsGoogle Scholar
  10. 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– 6361CrossRefGoogle Scholar
  11. 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– 135CrossRefGoogle Scholar
  12. 12.
    Dogan S, Turkoglu I (2008) Diagnosing hyperlipidemia using association rules. Mathematical and Computational Applications 13(3):193–202CrossRefGoogle Scholar
  13. 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 41:4434–4463CrossRefGoogle Scholar
  14. 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):158–167CrossRefGoogle Scholar
  15. 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):1019–1030CrossRefGoogle Scholar
  16. 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 EngineeringGoogle Scholar
  17. 17.
    Liu C, et al. (2011) Efficient network management for context-aware participatory sensing. SECON’11:116–124Google Scholar
  18. 18.
    Liu C, et al. (2013) Sketching the data center network traffic. IEEE Netw 27 (4):33–39CrossRefGoogle Scholar
  19. 19.
    Liu C, et al. (2014) Efficient naming, addressing and profile services in Internet-of-Things sensory environments. Ad Hoc Netw 18:85–101CrossRefGoogle Scholar
  20. 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):473–487CrossRefGoogle Scholar
  21. 21.
    Liu C, et al. (2014) A generic Admission-Control methodology for packet networks. IEEE Trans Wirel Commun 13(2):604–617MathSciNetCrossRefGoogle Scholar
  22. 22.
    Liu C, et al. (2015) Energy-Aware Participant selection for Smartphone-Enabled mobile crowd sensing. IEEE Syst J 99:1–12Google Scholar
  23. 23.
    Liu C, et al. (2015) Toward QoI and Energy Efficiency in Participatory Crowdsourcing. IEEE Trans Veh Technol 64(10):4684–4700CrossRefGoogle Scholar
  24. 24.
    Liu C, et al. (2016) Energy-Efficient Event detection by participatory sensing under budget constraints. IEEE Syst J 99:1–12Google Scholar
  25. 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, 13-15Google Scholar
  26. 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, 3-7Google Scholar
  27. 27.
    Shamim Hossain M (2015) Cloud-supported Cyber-Physical Framework for Patients Monitoring, IEEE Systems JGoogle Scholar
  28. 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– 202CrossRefGoogle Scholar
  29. 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 ApplicationsGoogle Scholar
  30. 30.
    Sheng Z, et al. (2015) Energy-efficient relay selection for cooperative relaying in wireless multimedia networks. IEEE Trans Veh Technol 64(3):1156–1170MathSciNetCrossRefGoogle Scholar
  31. 31.
    Yurur O, et al. (2014) A survey of context-aware middleware designs for human activity recognition. IEEE Commun Mag 52(6):24–31CrossRefGoogle Scholar
  32. 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):42Google Scholar
  33. 33.
    Zhang B, et al. (2016) Privacy-preserving QoI-Aware Participant Coordination for Mobile Crowdsourcing. Comput Netw 101:29–41CrossRefGoogle Scholar
  34. 34.
    Zhang B, et al. (2016) Energy-Efficient Software-Defined Data collection by participatory sensing. IEEE Sensors Journal 99:1–1Google Scholar
  35. 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):52–57CrossRefGoogle Scholar
  36. 36.
    Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning, PeerJ. pp.1251-1280Google Scholar
  37. 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 25:317–327CrossRefGoogle Scholar
  38. 38.
    Zhang Y, Wang S, et al. (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med Mater Eng 26:1283–1290CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ying Hu
    • 1
  • Kui Duan
    • 2
    Email author
  • Yin Zhang
    • 3
  • M. Shamim Hossain
    • 4
  • Sk Md Mizanur Rahman
    • 5
  • Abdulhameed Alelaiwi
    • 4
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School HospitalHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  4. 4.Software Engineering Department, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia
  5. 5.Information Systems Department, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations