Pregnancy Risk Level Classification Using The CRISP-DM Method

  • Reka Dwi Syaputra (1*) universitas budi luhur
  • Achmad Solichin (2) Universitas Budi Luhur

  • (*) Corresponding Author
Keywords: pregnancy risk level, classification, decision tree, random forest

Abstract

Independent midwife practices have the task of reminding and maintaining the quality of standardized reproductive health services for pregnant women. Independent midwife practices have had patient visits since the covid-19 pandemic from 2020 to 2021, especially at the yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. The covid-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. Maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting covid-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decisions. So that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. In solving this problem, the research takes a machine-learning approach. The research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. This study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method.

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Published
2022-12-14
How to Cite
Syaputra, R., & Solichin, A. (2022). Pregnancy Risk Level Classification Using The CRISP-DM Method. Jurnal Riset Informatika, 5(1), 537-548. https://doi.org/10.34288/jri.v5i1.487
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