Disease Risk Prediction using SVM based on Geographical Location

  • Authors

    • Abarna A R
    • A Umamakeswari
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.11989
  • Support Vector Machine, Geo location, Health care, Big Data, Disease Prediction.

  • Abstract

    Nowadays, people get more useful information from the internet and other technological platforms like social media. Plenty of health-care related information is available in social media where people spend more time in it. The existing methodology doesn’t include location in particular the user similarity based on the attributes. The proposed method specifies the assessment of disease risk by Support Vector Machine (SVM) algorithm to identify the similarity between the users based on the geographical location and then recommends the health expert to the users. This method also identifies the fake users and validates them. The health-care associated with big-data can be performed effectively in the proposed framework. The experimental output shows that the proposed method is more effective when compared with Collaborative Filtering based Disease Risk Assessment.

     

     

  • References

    1. [1] Ishwarappa and J. Anuradha, “A brief introduction on big data 5Vs characteristics and hadoop technologyâ€, Procedia Comput. Sci., vol. 48, C, pp. 319–324, 2015.

      [2] A. Abbas, K. Bilal, L. Zhang, and S. U. Khan, “A cloud based health insurance plan recommendation system: A user centered approachâ€, Futur. Gener. Comput. Syst., vol. 43–44, pp. 99–109, 2015.

      [3] P. Janardhanan, L. Heena, and F. Sabika, “Effectiveness of support vector machines in medical data miningâ€, J. Commun. Softw. Syst., vol. 11, no. 1, pp. 25–30, 2015.

      [4] K. Zhao, J. Yen, G. Greer, B. Qiu, P. Mitra, and K. Portier, “Finding influential users of online health communities: a new metric based on sentiment influenceâ€, J. Am. Med. Inform. Assoc., vol. 21, no.2, 2014.

      [5] A. Abbas, M. Ali, M. U. Shahid Khan, and S. U. Khan, “Personalized healthcare cloud services for disease risk assessment and wellness management using social mediaâ€, Pervasive Mob. Comput., vol. 28, pp. 81–99, 2016.

      [6] M. Khalilia, S. Chakraborty, and and M. Popescu, “Predicting disease risks from highly imbalanced data using random forestâ€, BMC Med. Inform. Decis. Mak., vol. 11, no. 1, 2011.

      [7] L. Ren and W. Wang, “An SVM-based collaborative filtering approach for Top-N web services recommendationâ€, Futur. Gener. Comput. Syst., vol. 78, pp. 531–543, 2018.

      [8] ated diagnosis of coronary artery disease (CAD) patients using optimized SVMâ€, Comput. Methods Programs Biomed., vol. 138, pp. 117–126, 2017.

      [9] W. Yu, T. Liu, R. Valdez, M. Gwinn, and M. J. Khoury, “Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetesâ€, BMC Med. Inform. Decis. Mak., vol. 10, no. 1, 2010.

      [10] J. Shang, M. Chen, H. Ji, D. Zhou, H. Zhang, and M. Li, “Dominant trend based logistic regression for fault diagnosis in nonstationary processesâ€, Control Eng. Pract., vol. 66, January, pp. 156–168, 2017.

      [11] A. Delgoshaei and C. Gomes, “A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain costâ€, Appl. Soft Comput. J., vol. 49, pp. 27–55, 2016.

      [12] A. Abbas, M. U. S. Khan, M. Ali, S. U. Khan, and L. T. Yang, “A cloud based framework for identification of influential health experts from twitterâ€, IEEE 15th Int. Conf. Scalable Comput. Commun. 20, pp. 831–838, 2016.

      [13] M. Hilbert, “Big Data for Developmentâ€, Dev. Policy Rev. Overseas Dev. Inst., vol. 1, no. 1, pp. 2012–2013, 2013.

      [14] S. Chung Chow, “On Big-Data Analytics in Biomedical Researchâ€, J. Biom. Biostat., vol. 6, no. 3, 2015.

      [15] A. Kotov and A. Kotov, “Social Media Analytics for Healthcare Chapter 1 Social Media Analytics for Healthcareâ€, 2015.

      [16] S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniquesâ€, 2008 IEEE/ACS Int. Conf. Comput. Syst. Appl., pp. 108–115, 2008.

      [17] https://www.scopus.com/authid/detail.uri?authorId=57195624326&origin=resultslist&zone=contextBox" target="_blank">Dhivyalakshmi, S. https://www.scopus.com/authid/detail.uri?authorId=42262789400&origin=resultslist&zone=contextBox" target="_blank">Umamakeswari, A. “https://www.scopus.com/record/display.uri?eid=2-s2.0-85029361610&origin=resultslist&zone=contextBox" target="_blank">The role of big data analytics in hospital management systemâ€, https://www.scopus.com/sourceid/19700182690?origin=recordpage">International Journal of Pure and Applied Mathematics, vol.115, no. 7 Special Issue, 2017, Pages 31-35.

      [18] S. Gurajala, J. S. White, B. Hudson, and J. N. Matthews, “Fake Twitter Accounts: Profile Characteristics Obtained Using an Activity-based Pattern Detection Approachâ€, Proc. 2015 Int. Conf. Soc. Media Soc., p. 9:1--9:7, 2015.

      [19] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithmsâ€, Proc. tenth Int. Conf. World Wide Web - WWW ’01, pp. 285–295, 2001.

      [20] E. F. Erkan, Ç. Teke, and D. Güleryüz, “A Fuzzy Expert System for Risk Self-Assessment of Chronic Diseasesâ€, vol. 18, no. 6, pp. 29–33, 2016.

      [21] https://www.scopus.com/authid/detail.uri?authorId=57195624326&origin=resultslist&zone=contextBox" target="_blank">Dhivyalakshmi, S. https://www.scopus.com/authid/detail.uri?authorId=42262789400&origin=resultslist&zone=contextBox" target="_blank">Umamakeswari, A. https://www.scopus.com/authid/detail.uri?authorId=55762534600&origin=resultslist&zone=contextBox" target="_blank">Aishwarya, https://www.scopus.com/authid/detail.uri?authorId=57191042376&origin=resultslist&zone=contextBox" target="_blank">Subramanian, E.R.S. https://www.scopus.com/authid/detail.uri?authorId=57191039966&origin=resultslist&zone=contextBox" target="_blank">Gurubaran, B. https://www.scopus.com/authid/detail.uri?authorId=57195632243&origin=resultslist&zone=contextBox" target="_blank">Shailesh Dheep, G., “Data analytics for smart HMISâ€, https://www.scopus.com/sourceid/19700182690?origin=recordpage">International Journal of Pure and Applied Mathematics, vol.115, no. 7 Special Issue, 2017, Pages 167-173.

      [22] T. Padmapriya and V. Saminadan, “Inter-cell Load Balancing technique for multi-class traffic in MIMO-LTE-A Networksâ€, International Journal of Electrical, Electronics and Data Communication (IJEEDC), ISSN: 2320- 2084, vol.3, no.8, pp. 22-26, Aug 2015.

      [23] S.V.Manikanthan and D.Sugandhi “Interference Alignment Techniques For Mimo Multicell Based On Relay Interference Broadcast Channel, â€International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume- 7, Issue 1 –MARCH 2014.

  • Downloads

  • How to Cite

    A R, A., & Umamakeswari, A. (2018). Disease Risk Prediction using SVM based on Geographical Location. International Journal of Engineering & Technology, 7(2.24), 11-15. https://doi.org/10.14419/ijet.v7i2.24.11989

    Received date: 2018-04-24

    Accepted date: 2018-04-24

    Published date: 2018-04-25