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Modeling the Percentage of Tuberculosis Cure in Indonesia Using a Multivariate Adaptive Regression Spline Approach

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  • معلومة اضافية
    • Contributors:
      Universitas Airlangga
    • بيانات النشر:
      Department of Statistics ITS
    • الموضوع:
      2024
    • Collection:
      Institut Teknologi Sepuluh Nopember (ITS): IPTEK Journals
    • نبذة مختصرة :
      Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium Tuberculosis. After India, Indonesia is the country with the second highest number of TB sufferers in the world. TB prevention efforts in Indonesia have been carried out, even since 1995. However, in general, 2006-2022 the TB cure in Indonesia tends to experience a downward trend. Therefore, it is important to know what variables have a significant effect and how the pattern relates to the percentage of TB cures. We urgently need this information to optimize TB handling efforts and achieve Sustainable Development Goals (SDGs) point 3, which focuses on good health and well-being. For that purpose, this study used the Multivariate Adaptive Regression Spline (MARS) approach. MARS is considered more flexible in overcoming cases of predictor variables that do not form a certain pattern to their response variables and can accommodate possible interactions between predictor variables. The best model was obtained at BF=18,MI=2, and MO=0 with minimum GCV value is 37.053 and R^2 is 91.6%, with significant predictor variables are food management sites meet the requirements according to standards, complete treatment, smoking population over 15 years, families with healthy latrines, and districts/municipalities implement healthy living germas policy. The significance of the nine predictors should prioritize enhancing the quality of health services for example ensuring a fair distribution of complete treatment for TB patients.
    • File Description:
      application/pdf
    • Relation:
      https://iptek.its.ac.id/index.php/inferensi/article/view/20344/8484; https://iptek.its.ac.id/index.php/inferensi/article/view/20344
    • الرقم المعرف:
      10.12962/j27213862.v7i2.20344
    • الدخول الالكتروني :
      https://iptek.its.ac.id/index.php/inferensi/article/view/20344
      https://doi.org/10.12962/j27213862.v7i2.20344
    • Rights:
      Authors who publish with this journal agree to the following terms:Authors retain the copyright and full publishing rights without restrictions under a Creative Commons Attribution-ShareAlike 4.0 International License.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
    • الرقم المعرف:
      edsbas.5BC37A4C