DIAGNOSIS PENYAKIT PREEKLAMPSIA PADA IBU HAMIL BERDASARKAN ALGORITME RIPPER

Priyadi, Deni (2019) DIAGNOSIS PENYAKIT PREEKLAMPSIA PADA IBU HAMIL BERDASARKAN ALGORITME RIPPER. Other thesis, Universitas Amikom Purwokerto.

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Abstract

Preeclampsia is one of the leading causes of death in pregnant women, in addition to bleeding and infection. Preeclampsia is a unique disease because it only occurs in pregnant women. Preeclampsia is known as a "disease of theories" because there are many theories that explain the causes of preeclampsia and the exact cause of this is unknown. Several risk factors have been identified that can increase the risk of preeclampsia. This makes researchers and practitioners focus on detecting / diagnosing preeclampsia, because this disease is one of the third highest causes of death in ASEAN. In this study using rules obtained from the RIPPER algorithm to diagnose preeclampsia using a medical record dataset of pregnant women. And then it is used to compare the performance between testing the RMIH dataset by using the missing values handling without handling missing values. From the test, the results of the accuracy were 98,9%, 97,8% sensitivity, and 100% specificity, with no handling of missing values. The results of the same dataset evaluation using the handling of missing values produced the same accuracy of 98,9%. From these results it can be concluded that testing the RMIH dataset using the RIPPER algorithm method by handling missing values or datset missing values has the same accuracy value.

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