Analysis Cataract Patients Databases for Bioinformatics

  • Authors

    • Zaki .S. Tywofik
    • Abdul Karem Thamer Mohammed
    https://doi.org/10.14419/ijet.v7i2.9.9392
  • Data Mining, Classification, Decision Tree Algorithm, Weka tool
  • The aims at using data mining in the data of Cataract derived from the report prepared by sample for many females and males the  Health Organization to suggest the most effective ways to treat Cataract according to the age-old. Believe that the expected results of the research will be of medical benefit for Oral or Insulin for two main reasons. Doctors usually do not have the time or the good enough to scrutinize a large number of data to derive new rules for treatment, and therefore the use of data mining will extract the rules and the rules of the doctors. A cataract can cause a decline in visual capacity, which thus can be named a visual inability. Cataracts can be characterized in three different ways. The main definition is a target focal point change. The second is a focal point haziness that is related with a characterized dimension of visual sharpness misfortune. The third identifies with the useful results of focal point placations. This rule centers around the last definition. It manages care of the patient with useful disability because of Cataracts and enhancement in capacity because of treatment for the condition. Considering the pervasiveness of cataract among male and female the investigation is gone for discovering the attributes that decide the nearness of cataract and to follow the greatest number of people experiencing cataract test of 18 people. In this paper the information characterization is Cataracts patients informational collection is created by gathering information from doctor's facility.   

  • References

    1. [1]. Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), 44-47.

      [2]. C.sugandhi, P.Yasodha, M.Kannan "Analysis of a Population of cataracts

      Patients Databases in Weka Tool" International Journal of Scientific & Engineering Research Volume 2, Issue 10, Oct-2011

      [3]. José M. Quintana, MD. Inmaculada Arostegui. Txomin Alberdi. "Decision Trees for Indication of Cataract Surgery Based on Changes in Visual Acuity" 2010.

      [4]. Cios, K.J. & Moore, G.W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26(1), 1-24.

      [5]. M.D Twa,s. Parthasarathy, T,W. Raasch, and M.A. Bullimore, "Automated classification of keratoconus: A case study in analzing clinical data," in SIAM int'I conference on data mining, San Francisco, CA, 2003.

      [6]. Lambert SR. "Changes in ocular growth after pediatric cataract surgery" Dev Ophthalmol. 2016;57:29-39.

      [7]. Rafalski, E. (2002). Using data mining and data repository methods to identify marketing opportunities in healthcare. Journal of Consumer Marketing, 19(7), 607-613..

      [8] M. H. Ali, M. F. Zolkipli, M. A. Mohammed, and M. M. Jaber, “Enhance of extreme learning machine-genetic algorithm hybrid based on intrusion detection system,†J. Eng. Appl. Sci., vol. 12, no. 16, 2017.

      [9] M. H. Ali, M. F. Zolkipli, M. M. Jaber, and M. A. Mohammed, “Intrusion detection system based on machine learning in cloud computing,†J. Eng. Appl. Sci., vol. 12, no. 16, 2017.

      [10] M. M. JABER, A. B. D. Ghani, M. A. Mohammed, and M. A. Burhanuddin, “A Survey on Adoption Theories : Toward Building Developing Countries Telemedicine,†Biomed. Res., 2017.

      [11] T. Abbas, A. S. Shibghatullah, R. Yusof, and M. M. Jaber, “Effective environmental factors to performance of electronic information sharing in iraqi intelligence,†J. Eng. Appl. Sci., vol. 11, no. 3, 2016.

      [12] M. K. A. Ghani et al., “Analysis of healthcare system in Iraq,†Soc. Sci., vol. 11, no. 11, 2016.

  • Downloads

  • How to Cite

    Tywofik, Z. .S., & Mohammed, A. K. T. (2018). Analysis Cataract Patients Databases for Bioinformatics. International Journal of Engineering & Technology, 7(2.9). https://doi.org/10.14419/ijet.v7i2.9.9392