IRS-HD: An Intelligent Personalized Recommender System for Heart Disease Patients in a Tele-Health Environment

  • Raid Lafta
  • Ji Zhang
  • Xiaohui Tao
  • Yan Li
  • Vincent S. Tseng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)

Abstract

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day.

References

  1. 1.
    Kuh, D., Shlomo, Y.B.: A Life Course Approach to Chronic Disease Epidemiology. Oxford University Press, Oxford (2004)CrossRefGoogle Scholar
  2. 2.
    Hsieh, N.-C., Hung, L.-P., Shih, C.-C., Keh, H.-C., Chan, C.-H.: Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques. J. Med. Syst. 36(3), 1809–1820 (2012)CrossRefGoogle Scholar
  3. 3.
    Geng, H., Lu, T., Lin, X., Liu, Y., Yan, F.: Prediction of protein-protein interaction sites based on naive bayes classifier. Biochem. Res. Int. 2015 (2015)Google Scholar
  4. 4.
    Snchez, A.S., Iglesias-Rodrguez, F., Fernndez, P.R., de Cos Juez, F.: Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. Int. J. Ind. Ergon. 52, 92–99 (2016)CrossRefGoogle Scholar
  5. 5.
    Kim, J.-K., Lee, J.-S., Park, D.-K., Lim, Y.-S., Lee, Y.-H., Jung, E.-Y.: Adaptive mining prediction model for content recommendation to coronary heart disease patients. Cluster Comput. 17(3), 881–891 (2014)CrossRefGoogle Scholar
  6. 6.
    Myers, J., de Souza, B.-S.C.R., Guazzi, M., Chase, P., Bensimhon, D., Peberdy, M.A., Ashley, E., West, E., Cahalin, L.P.: A neural network approach to predicting outcomes in heart failure using cardiopulmonary exercise testing. Int. J. Cardiol. 171(2), 265–269 (2014)CrossRefGoogle Scholar
  7. 7.
    Bashir, S., Qamar, U., Khan, F.H.: BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas. Phys. Eng. Sci. Med. 38(2), 305–323 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Raid Lafta
    • 1
  • Ji Zhang
    • 1
  • Xiaohui Tao
    • 1
  • Yan Li
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Faculty of Health, Engineering and SciencesUniversity of Southern QueenslandToowoombaAustralia
  2. 2.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan

Personalised recommendations