International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 10 | Oct 2022
p-ISSN: 2395-0072
www.irjet.net
Comparative Analysis of Various Algorithms for Fetal Risk Prediction Jay Mistry1 1
Fourth Year Student, Information Technology Department, Thadomal Shahani Engineering College, Bandra(W), Mumbai - 400050 ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Fetal problems emerge while your unborn child
bank loan applications as safe or risky, or a prediction model to estimate how much money a potential consumer will spend on computer equipment.
grows within the womb. Congenital refers to the fact that a child is born with these conditions. Some fetal disorders are inherited genetically or from a parent. A machine learning approach for predicting if a woman is having a high risk fetus is needed. We have collected the data from an online repository. The data is been balanced and pre-processed for better prediction. This dataset is being used on machine learning algorithms like Random Forest, Bagging, AdaBoostM1, SMO, Kstar, Naïve Bayes, Hoeffding Tree and Classification via Regression to build a model which will help in predicting the fetal condition. All of these algorithms is compared with specific performance metrics and the best result is showcased.
Key Words:
Fetal, performance metrics
pre-processed,
1.1 Motivation Considering all the tests that used to detect fetal abnormalities, very few of them are actually reliable. Prenatal tests are not always perfect. The data for false-positives or false-negatives varies from test to test. These procedures might carry a real risk of miscarriage because an amount of amniotic fluid or tissue from around the fetus is needed.
1.2 Problem Statement An analysis of various machine learning algorithms to select which algorithm can accurately predict the level of risk in fetus based on few performance metrics.
prediction,
1. INTRODUCTION
2. Related Work
As included in NCI dictionary of Cancer terms, fetus is defined as the unborn baby that develops and grows inside the uterus in humans. The fetal period begins 8 weeks after fertilization of an egg by a sperm and ends at the time of birth. There might be cases where a pregnant lady could have pregnancy risks or distressing conditions for the fetus. This distress is may be due to low levels of amniotic fluids, high levels of amniotic fluids, placental abruption, uncontrolled diabetes and pregnancy lasting for more than 40 weeks. Fetal surgery may be advantageous for babies with specific birth abnormalities. Our specialists carry out these extremely difficult treatments while your unborn child is still in the womb. There are tests that are used to assess the fetal health such as fetal movement counts, biophysical profile, contraction stress test and Doppler ultrasound exam of the umbilical artery. A method for keeping track of uterine contractions and fetal heart rate during pregnancy is called Cardiotocography, or CTG. It is used to evaluate the health of the fetus and to spot fetal distress early.
In [10] J. Li and X. Liu have implemented twelve machine learning algorithms on CTG dataset. The proposed model has performed brilliantly in different classification model evaluations. The four top models are then combined to create the Blender Model using the soft voting integration method, which is then contrasted with the stacking integration method. The model described in this study outperformed the conventional machine learning models in a variety of Classification Model evaluations, achieving accuracy rates of 0.959, AUCs of 0.988, recall rates of 0.916, precision rates of 0.959, F1s of 0.958, and MCCs of 0.886. In [7] R. Chinnaiyan and S. Alex have used Machine Learning algorithm to build a predictive classifier to forecast the fetal health and growth state from a set of pre-classified patterns knowledge. The majority of this evaluation of the literature focuses on fetal anomalies that occur during the first trimester of pregnancy. The major goal of this article review is to investigate the various machine learning processes for accurate diagnosis and prognosis of abdominal anomalies in order to lower the incidence rate. Tested machine learning algorithms save time and effort while delivering more precise results. Segmentation, Image Enhancement, Feature Extraction, and Image Classification are used to accomplish this.
To create a model that represents the various data classes and forecasts future data trends, classification and prediction are used. With the aid of prediction models, classification forecasts the category labels of data. We have the clearest knowledge of the data at a broad scale thanks to this analysis. Prediction models predict continuous-valued functions, whereas classification models predict categorical class labels. For instance, based on a person's income and line of work, we can create a classification model to classify
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In [6] the results of the tests display a classifier model to be 83% and 84% accurate, before and after feature selection,
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