Book ChapterDOI
Prediction of Pregnancy-Induced Hypertension Levels Using Machine Learning Algorithms
Anuja Hiwale,Pratvina Talele,Rashmi Phalnikar +2 more
- pp 597-608
Reads0
Chats0
TLDR
This work shows that the accuracy achieved by the use of decision tree (90%) is better than that of support vector machine (86.667%) and logistics regression (83.334%) algorithms used in earlier work.Abstract:
Pregnancy-induced hypertension (PIH) is a foremost reason for disease and death in maternal, fetal, and neonatal babies. Women having PIH are at greater risk of intrauterine growth retardation in fetuses, premature delivery of a baby, and intrauterine death. Machine Learning has been widely used in an array of applications in the healthcare domain for analyzing data. The aim of this study by the authors is to predict the PIH levels using supervised learning algorithms with an aim to prevent PIH-related complications. The study works on a data set of about 100 pregnant women between the age group of 18–32. The data set uses 19 predictor variables like body surface area (BSA), pulse rate (PR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). SBP and DBP variables are considered to predict the PIH level of the pregnant woman. This work shows that the accuracy achieved by the use of decision tree (90%) is better than that of support vector machine (86.667%) and logistics regression (83.334%) algorithms used in earlier work.read more
Citations
More filters
Journal ArticleDOI
A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units
TL;DR: A real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure that enables timely interventions and improved healthcare services is proposed and compared with the state-of-the-art systems employing logistic regression.
References
More filters
Journal ArticleDOI
Hypertensive disorders in pregnancy
TL;DR: The incidences, prevalences, definitions, cardinal signs, and patients at increased risk for hypertensive disorders in pregnancy are discussed and successful management of these disorders is early detection.
Journal ArticleDOI
Current best practice in the management of hypertensive disorders in pregnancy.
TL;DR: This review presents the current best practice in diagnosis and management of preeclampsia and the hypertensive disorders of pregnancy.
Journal ArticleDOI
Hypertension in pregnancy: A community-based study
TL;DR: Nearly one in 14 pregnant women in rural areas of Haryana suffers from a hypertensive disorder of pregnancy, and early diagnosis and treatment through regular antenatal checkup is a key factor to prevent hypertensive disorders of pregnancy and its complications.
Journal ArticleDOI
Prediction of low birth weight using Random Forest: A comparison with Logistic Regression
Parisa Ahmadi,Hamid Alavi-Majd,Soheila Khodakarim,Leili Tapak,Nourossadat Kariman,Payam Amini,Forough Pazhuheian +6 more
TL;DR: The results of the present study showed that using Random Forest improved the prediction of low birth weight compared with Logistic Regression, because of the fact that the former accounts for all interactions between covariates.
Journal ArticleDOI
Associated risk factors with pregnancy-induced hypertension: A hospital-based KAP study
Vineeta Singh,Manushi Srivastava +1 more
TL;DR: Findings of the study shows that >50% (60.49%) of women are unaware about hypertension, and those women belong to the age group of 20-30, and they also diagnosed with prehypertension, which is a major cause to for hypertension.