Analysis of Pregnancy Risk Factors for Pregnant Women Using Analysis Data Based on Expert System

Krisnanik, Erly; Tambunan, Kraugusteeliana; Irmanda, Helena Nurramdhani

doi:10.1109/icimcis48181.2019.8985211

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…Some studies focus on observing pregnant women's conditions for analyzing and recording health risk factors while other aims at the prediction. For example, a pregnancy risk detection system (PRDS) is constructed to detect the pregnancy risk level based on the pregnant woman's experienced symptoms [16]. The research observations are conducted continuously over time at Panimbang health center.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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show abstract

“…(https://creativecommons.org/licenses/by-sa/4.0/) kehamilan. Hasil dari penelitian ini adalah dengan menggunakan sistem ini, bidan dapat mengantisipasi tingkat risiko ibu hamil melalui rekomendasi antisipasi terburuk sehingga berpotensi menurunkan angka kematian ibu hamil atau janin (Krisnanik, 2019). Menurut penelitian terkait oleh Supriyanti, tingginya angka kematian ibu dan bayi di negara berkembang, khususnya Indonesia, cukup memprihatinkan.…”

Section: Gambar 1 Penyebab Kematian Ibu Sumber: Pusdatin Kemenkes 2018unclassified

Sistem Pakar Diagnosa Penyakit Pada Ibu Hamil Menggunakan Metode Forward Chaining

Sundari¹,

Ns.,MS

Budiarti

et al. 2023

J-INTECH

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AN EXPERT SYSTEM FOR DISEASE DIAGNOSIS PREGNANT WOMEN USING FORWARD CHAINING METHOD. Health is one of the important factors for pregnant women in conducting daily activities. However, some pregnant women do not care about their body condition so there are many complaints, causing a disease in pregnant women. Pregnant women can be affected by pregnancy, the disease can be known from the symptoms it causes, but to know precisely what diseases suffered by pregnant women, requiring an expert or specialist doctor Content. In this study designed a system of experts based on the website using a rule base with the method of forward chaining inference aimed at helping pregnant mothers in diagnosing early pregnancy disease. This expert system is able to diagnose the disease suffered by the pregnant mothers according to the question of the symptoms asked to be answered by the pregnant women, so that the system will provide diagnostic results, details on diseases and treatment The disease.

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A Miscarriage Prevention System Using Machine Learning Techniques

Biswas

Shukla

2021

Proceedings of Second Doctoral Symposium on Computational Intelligence

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Analysis of Pregnancy Risk Factors for Pregnant Women Using Analysis Data Based on Expert System

Cited by 7 publications

References 2 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Sistem Pakar Diagnosa Penyakit Pada Ibu Hamil Menggunakan Metode Forward Chaining

A Miscarriage Prevention System Using Machine Learning Techniques

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