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Rizky Amelia
Poltekkes Kemenkes Semarang, Indonesia
Environmental Science Health Professions Nursing Public Health

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Implementasi Data Mining Untuk Memprediksi Pre-Eklampsia Dalam Kehamilan Menggunakan Algoritma C4.5 Dhita Aulia Octaviani; Dhias Widiastuti; Rizky Amelia; Abu Salam
Jurnal Kesehatan Vol. 18 No. 1 (2025): Jurnal Kesehatan
Publisher : UPPM Poltekkes Kemenkes Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32763/ps5qpj75

Abstract

Background: Maternal Mortality Rate (MMR) is an indicator to see the success of maternal health efforts. Hypertension in pregnancy, including pre-eclampsia (PE), is the main cause of maternal death and is one of the pregnancy complications whose cases continue to increase. The development of technology and information can be utilized in the health sector. The data mining process can help determine pre-eclampsia status through pregnancy and childbirth medical record data. Purpose: identify PE risk factors using data mining analysis with the C4.5 algorithm. Method: Using the c4.5 algorithm to help determine the risk factors that most influence the incidence of pre-eclampsia with stages: data selection, data cleaning, data transformation, looking for data patterns, and evaluating results. Results: The results of the C4.5 algorithm indicate that a history of preeclampsia (PE) has a strong correlation with the occurrence of PE, with a value of 0.71, followed by a maternal history of hypertension and a family history of hypertension. Conclusion: Mothers without a history of PE but with a parity of >2 and obesity are likely to experience PE. Mothers with a history of hypertension/PE, a family history of hypertension/PE, a parity of >2, diabetes, and a birth interval of <2 years are likely to experience PE.
Co-Authors Abu Salam Dhias Widiastuti Dhita Aulia Octaviani