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A Structural Graph-Coupled Advanced Machine Learning Ensemble Model for Disease Risk Prediction in a Telehealthcare Environment

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Big Data in Engineering Applications

Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

The use of intelligent and sophistic technologies in evidence-based clinical decision making support have been playing an important role in improving the quality of patients’ life and helping to reduce cost and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a structural graph approach with advanced machine learning ensemble model is proposed for short-term disease risk prediction to provide chronic heart disease patients with appropriate recommendations about the need to take a medical test or not on the coming day based on analysing their medical data. A time series telehealth data recorded from patients is used for experimentations, evaluation and validation. The Tunstall dataset were collected from May to October 2012, from industry collaborator Tunstall. A time series data is segmented into slide windows and then mapped into undirect graph. The size of slide window was empirically determined. The structural properties of graph enter as the features set to the machine learning ensemble classifier to predict the patient’s condition one day in advance. A combination of three classifiers—Least Squares-Support Vector Machine, Artificial Neural Network, and Naive Bayes—are used to construct an ensemble framework to classify the graph features. To investigate the predictive ability of the graph with the ensemble classifier, the extracted statistical features were also forwarded to the individual classifiers for comparison. The findings of this study shows that the recommendation system yields a satisfactory recommendation accuracy, offers a effective way for reducing the risk of incorrect recommendations as well as reducing the workload for heart disease patients in conducting body tests every day. A 94% average prediction accuracy is achieved by using the proposed recommendation system. The results conclusively ascertain that the proposed system is a promising tool for analyzing time series medical data and providing accurate and reliable recommendations to patients suffering from chronic heart diseases.

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References

  1. Abegunde, D. O., Mathers, C. D., Adam, T., Ortegon, M., & Strong, K. (2007). The burden and costs of chronic diseases in low-income and middle-income countries. The Lancet, 370, 1929–1938.

    Article  Google Scholar 

  2. Artameeyanant, P., Sultornsanee, S., & Chamnongthai, K. (2015). Classification of electromyogram using weight visibility algorithm with multilayer perceptron neural network. In: 7th International Conference on Knowledge and Smart Technology (KST) (pp. 190–194). Chonburi, Thailand: IEEE.

    Google Scholar 

  3. Bashir, S., Qamar, U., & Khan, F. H. (2015). BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australasian Physical and Engineering Sciences in Medicine, 38, 305–323.

    Article  Google Scholar 

  4. Bernhardt, B. C., Bonilha, L., & Gross, D. W. (2015). Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy. Epilepsy and Behavior, 50, 162–170.

    Article  Google Scholar 

  5. Blondel, V. D., Gajardo, A., Heymans, M., Senellart, P., & Van Dooren, P. (2004). A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM Review, 46, 647–666.

    Article  MathSciNet  MATH  Google Scholar 

  6. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424, 175–308.

    Article  MathSciNet  MATH  Google Scholar 

  7. Braamse, A. M., Jean, C. Y., Visser, O. J., Heymans, M. W., van Meijel, B., Dekker, J., et al. (2016). Developing a risk prediction model for long-term physical and psychological functioning after hematopoietic cell transplantation. Biology of Blood and Marrow Transplantation, 22, 549–556.

    Article  Google Scholar 

  8. Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123–140.

    MATH  Google Scholar 

  9. 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 Systems with Applications, 38, 5507–5513.

    Article  Google Scholar 

  10. Chen, D., Jin, D., Goh, T. T., Li, N., & Wei, L. (2016). Context-awareness based personalized recommendation of anti-hypertension drugs. Journal of Medical Systems, 40, 202.

    Article  Google Scholar 

  11. Dewar, A. R., Bull, T. P., Malvey, D. M., & Szalma, J. L. (2017). Developing a measure of engagement with telehealth systems: The mHealth technology engagement index. Journal of Telemedicine and Telecare, 23, 248–255.

    Article  Google Scholar 

  12. Diykh, M., & Li, Y. (2016). Complex networks approach for EEG signal sleep stages classification. Expert Systems with Applications, 63, 241–248.

    Article  Google Scholar 

  13. Diykh, M., Li, Y., & Wen, P. (2016). EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24, 1159–1168.

    Article  Google Scholar 

  14. Fang, Z., & Wang, J. (2014). Efficient identifications of structural similarities for graphs. Journal of Combinatorial Optimization, 27, 209–220.

    Article  MathSciNet  MATH  Google Scholar 

  15. Gao, H., Jian, S., Peng, Y., & Liu, X. (2016). A subspace ensemble framework for classification with high dimensional missing data. Multidimensional Systems and Signal Processing, 1–16.

    Google Scholar 

  16. He, Y., & Evans, A. (2010). Graph theoretical modeling of brain connectivity. Current Opinion in Neurology, 23, 341–350.

    Google Scholar 

  17. Huang, F., Wang, S., & Chan, C.C. (2012). Predicting disease by using data mining based on healthcare information system. In: Granular Computing (GrC), (pp. 191–194). Hangzhou, China: IEEE.

    Google Scholar 

  18. Huang, X., & Lai, W. (2006). Clustering graphs for visualization via node similarities. Journal of Visual Languages and Computing, 17, 225–253.

    Article  Google Scholar 

  19. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31, 264–323.

    Article  Google Scholar 

  20. Kogge, P. M. (2016). Jaccard Coefficients as a Potential Graph Benchmark. In Parallel and Distributed Processing Symposium Workshops (pp. 921–928) Chicago: IEEE.

    Google Scholar 

  21. Krishnaiah, V., Narsimha, D. G., & Chandra, D. N. S. (2013). Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science and Information Technologies, 4, 39–45.

    Google Scholar 

  22. Kuh, D., & Shlomo, Y. B. (2004). A Life Course Approach to Chronic Disease Epidemiology. Oxford University Press.

    Google Scholar 

  23. Lafta, R., Zhang, J., Tao, X., Li, Y., & Tseng, V. S. (2015). An intelligent recommender system based on short-term risk prediction for heart disease patients. In Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 102–105). Singapore: IEEE.

    Google Scholar 

  24. Lafta, R., Zhang, J., Tao, X., Li, Y., Tseng, V. S., Luo, Y., et al. (2016). An intelligent recommender system based on predictive analysis in telehealthcare environment. Web Intelligence, 4, 325–336.

    Article  Google Scholar 

  25. Lang, S. (2017). Cognitive eloquence in neurosurgery: Insight from graph theoretical analysis of complex brain networks. Medical Hypotheses, 98, 49–56.

    Article  Google Scholar 

  26. Li, X., Hu, X., Jin, C., Han, J., Liu, T., Guo, L., et al. (2013). A comparative study of theoretical graph models for characterizing structural networks of human brain. International journal of biomedical imaging, 2013.

    Google Scholar 

  27. Li, S., Tang, B., & He, H. (2016). An imbalanced learning based MDR-TB early warning system. Journal of Medical Systems, 40, 164.

    Article  Google Scholar 

  28. Micheloyannis, S., Pachou, E., Stam, C. J., Vourkas, M., Erimaki, S., & Tsirka, V. (2006). Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neuroscience Letters, 402, 273–277.

    Article  Google Scholar 

  29. Miraglia, F., Vecchio, F., & Rossini, P. M. (2017). Searching for signs of aging and dementia in EEG through network analysis. Behavioural Brain Research, 317, 292–300.

    Article  Google Scholar 

  30. Mohktar, M. S., Redmond, S. J., Antoniades, N. C., Rochford, P. D., Pretto, J. J., Basilakis, J., et al. (2015). Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data. Artificial Intelligence in Medicine, 63, 51–59.

    Article  Google Scholar 

  31. Njie, G. J., Proia, K. K., Thota, A. B., Finnie, R. K., Hopkins, D. P., Banks, S. M., et al. (2015). Clinical decision support systems and prevention: A community guide cardiovascular disease systematic review. American Journal of Preventive Medicine, 49, 784–795.

    Article  Google Scholar 

  32. Panzica, F., Varotto, G., Rotondi, F., Spreafico, R., & Franceschetti, S. (2013). Identification of the epileptogenic zone from stereo-EEG signals: A connectivity-graph theory approach. Frontiers in Neurology, 4.

    Google Scholar 

  33. Polat, K., & Gne, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing, 17, 694–701.

    Article  Google Scholar 

  34. Sarsoh, J. T., Hashem, & K. M. (2012). Classifying of human face images based on the graph theory concepts. Global Journal of Computer Science and Technology, 12.

    Google Scholar 

  35. Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1, 27–64.

    Article  MATH  Google Scholar 

  36. Snchez, A. S., Iglesias-Rodrguez, F. J., Fernndez, P. R., & de Cos, Juez F. (2016). Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. International Journal of Industrial Ergonomics, 52, 92–99.

    Article  Google Scholar 

  37. Stam, C. J., & Reijneveld, J. C. (2007). Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics, 1, 3.

    Article  Google Scholar 

  38. Thong, N. T. (2015). HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Systems with Applications, 42, 3682–3701.

    Article  Google Scholar 

  39. Tuffry, S. (2011). Data Mining and Statistics For Decision Making, Wiley Chichester.

    Google Scholar 

  40. Valentini, G., Masulli, F. (2002). Ensembles of learning machines. In Italian Workshop on Neural Nets (pp. 3–20). Heidelberg: Springer.

    Chapter  MATH  Google Scholar 

  41. Vecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R., Caulo, M., et al. (2017). Small World architecture in brain connectivity and hippocampal volume in Alzheimers disease: A study via graph theory from EEG data. Brain Imaging and Behavior, 11, 473–485.

    Article  Google Scholar 

  42. Verma, L., Srivastava, S., & Negi, P. (2016). A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of Medical Systems, 40, 1–7.

    Article  Google Scholar 

  43. Vural, C., & Yildiz, M. (2010). Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis. Journal of Medical Systems, 34, 83–89.

    Article  Google Scholar 

  44. Wang, J., Qiu, S., Xu, Y., Liu, Z., Wen, X., Hu, X., et al. (2014). Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy. Clinical Neurophysiology, 125, 1744–1756.

    Article  Google Scholar 

  45. Wang, J., Qiu, M., & Guo, B. (2017). Enabling real-time information service on telehealth system over cloud-based big data platform. Journal of Systems Architecture, 72, 69–79.

    Article  Google Scholar 

  46. Yang, J. G., Kim, J. K., Kang, U. G., & Lee, Y. H. (2014). Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFISLDA). Personal and Ubiquitous Computing, 18, 1351–1362.

    Article  Google Scholar 

  47. Yeh, D. Y., Cheng, C. H., & Chen, Y. W. (2011). A predictive model for cerebrovascular disease using data mining. Expert Systems with Applications, 38, 8970–8977.

    Article  Google Scholar 

  48. Zhang, J., Lafta, R., Tao, X., Li, Y., Zhu, X., Luo, Y., et al. (2017). Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. 5, 10674–10685.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, Australia

    Raid Lafta, Ji Zhang, Xiaohui Tao, Yan Li & Mohammed Diykh

  2. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School China, Shenzhen, China

    Jerry Chun-Wei Lin

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  1. Raid Lafta
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  2. Ji Zhang
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  3. Xiaohui Tao
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  4. Yan Li
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  5. Mohammed Diykh
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  6. Jerry Chun-Wei Lin
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Corresponding author

Correspondence to Ji Zhang .

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Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

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Lafta, R., Zhang, J., Tao, X., Li, Y., Diykh, M., Lin, J.CW. (2018). A Structural Graph-Coupled Advanced Machine Learning Ensemble Model for Disease Risk Prediction in a Telehealthcare Environment. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_18

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  • DOI: https://doi.org/10.1007/978-981-10-8476-8_18

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  • Online ISBN: 978-981-10-8476-8

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