ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK BASED MATERNAL AND FETAL HEALTH RISK LEVEL PREDICTION AND SENSITIVITY ANALYSIS DURING PREGNANCY
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Date
2022-11-30
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Abstract
Health of women throughout pregnancy, childbirth, and the postpartum period is referred to as
maternal health. To ensure that women and their unborn children achieve their maximum potential
for health and wellbeing, each stage should be enjoyable. The majority of pregnancies and
deliveries are successful, but complications do happen occasionally and when they do, they can
have devastating effects on both mothers and infants. Through a better understanding of risk
factors, increased surveillance, and more early and suitable interventions, predictive modeling has
the potential to enhance outcomes and assist gynecologists in providing better care. Dataset used
for analysis is collected from the local hospitals of Noakhali District. For the analysis, the main
risk factors considered are age, Blood pressure (systolic), Blood pressure (diastolic), Body
temperature, maternal heart rate, blood glucose, hepatitis B, TSH, serum SGPT, serum uric acid,
fetal heart rate, amount of amniotic fluid ,fetal movement and obesity. The dataset contains the
details of these features of women during their pregnancy. Data preprocessing is done by mapping
naming value, handling missing value by prediction, selecting important feature and feature
scaling etc. Correlation is also checked for numerical values. Data is then divided for training and
testing, and the best accuracy for each prediction model is then determined. These models include
Multinomial Logistic Regression, Naive Bayes, K-Nearest-Neighbors, Support Vector Machine
(SVM), Decision Tree, Random Forest, and Neural Network Multilayer Perceptron. Accuracy of
MLR, MNB, KNN, SVM, DT, RF and MLP algorithm is 89.58%, 80.13% , 100.0%, 90.23%,
100.0%, 100.0% and 100.0% respectively. The DT, RF and MLP algorithms also gives the best
precision, recall, f1-score and ROC_AUC score i.e. all are 100.0%. In comparison to other
algorithms, the DT, RF, and MLP perform best in terms of accuracy, precision, recall, f1-score,
and ROC_AUC score. The input and target variables will also be subjected to sensitivity analysis
to determine which input parameters have the greatest impact on risk. According to sensitivity
analysis, the following factors have a significant impact on the risk to the mother's and the fetus's
health: blood pressure(systolic & diastolic) fetal movement, fetal heart rate, and the amount of
amniotic fluid. By avoiding maternal and child morbidity during pregnancy for areas of
Bangladesh, this effort will help to improve healthcare for expectant mothers and their fetuses.