Modeling Female Contraceptive Recommendation Using Hybrid Analytical Hierarchy Process and Profile Matching
Abstract
Multi-criteria decision-making (MCDM) methodologies have been extensively employed across various domains within healthcare. They can be utilized for disease prediagnosis, aiding clinical decision-making (e.g., surgery), conducting health technology assessments, as well as establishing healthcare priorities. This research presents the outcomes of a hybrid MCDM strategy, integrating the AHP and Profile Matching to facilitate clinical recommendations related to female contraception. Many cases in Indonesia show acceptors' inappropriateness in using available contraceptives, causing side effects resulting in negative effects. The challenges in the Keluarga Berencana or Planned Parenthood program in Indonesia are increasing, based on the decline in the number of new acceptors and the high unmet needs for contraception. Failure to meet the need for contraception has the potential to increase birth rates and maternal mortality rates, which requires serious attention and the development of appropriate strategies. Based on the problem, this study aims to create a decision support model in selecting suitable contraceptives for acceptors. The criteria used in this study consisted of age, medical history, weight (BMI), breastfeeding or not, history of childbirth, period of use, and income. The seven criteria are implemented in AHP with a consistency test result value of 2.2%. Based on the target value of contraceptives obtained from the results of Profile Matching, compatibility was determined with a sample of three acceptor profiles. The results that have been achieved indicate a sample recommendation model for acceptors of IUD-type contraception that can assist midwives or medical personnel in providing recommendations for selecting appropriate contraceptive methods. Future studies can integrate the results of recommendations for health service providers (e.g., hospitals, Public Health Center or Puskesmas) in procuring contraceptives.
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Journal of Applied Data Sciences
ISSN | : | 2723-6471 (Online) |
Organized by | : | Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia. |
Website | : | http://bright-journal.org/JADS |
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support@bright-journal.org (technical issues) |
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