Diagnosis of Preeklamsia In Pregnant Women Based On K-Nearest Neighbor Algorithm

Diagnosis Preeklamsia Pada Ibu Hamil Berdasarkan Algoritme K- Nearest Neighbour

Authors

  • Rifki Hidayat Author
  • Tri Astuti Universitas Amikom Purwokerto

DOI:

https://doi.org/10.33481/infomans.v14i2.153

Keywords:

Confusion Matrix, Data Mining, KNN Algorithm, Preeclampsia

Abstract

Maternal deaths are divided into two namely direct and indirect deaths. Globally 80% of direct maternal deaths, preeclampsia are included in direct maternal deaths. Preeclampsia conditions of pregnancy with hypertension occur after the 20th week in women who previously had normal blood pressure. Preeclampsia can also be characterized by hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) accompanied by proteinuria (≥ 300 mg / dl in tamping urine 24 hours). In this study, an analysis of medical records in the Purbalingga and Banyumas areas using 8 attributes, namely age, body weight, blood pressure, edema, multiple pregnancy, history of hypertension, how many children, urine protein, and preeclampsia class. From calculations using the K-NN (K-Nearest Neighbour) algorithm, the Sensitivity performance value of 98.19%, Specificity 100%, and Accuracy 98.33%.

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Published

2020-11-17

How to Cite

Rifki Hidayat, & Astuti, T. (2020). Diagnosis of Preeklamsia In Pregnant Women Based On K-Nearest Neighbor Algorithm: Diagnosis Preeklamsia Pada Ibu Hamil Berdasarkan Algoritme K- Nearest Neighbour. Infoman’s : Jurnal Ilmu-Ilmu Manajemen Dan Informatika, 14(2), 106 - 116. https://doi.org/10.33481/infomans.v14i2.153

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