ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK BASED MATERNAL AND FETAL HEALTH RISK LEVEL PREDICTION AND SENSITIVITY ANALYSIS DURING PREGNANCY
| dc.contributor.author | Zarin Tanzim | |
| dc.date.accessioned | 2025-11-23T04:26:15Z | |
| dc.date.available | 2025-11-23T04:26:15Z | |
| dc.date.issued | 2022-11-30 | |
| dc.description.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. | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/114 | |
| dc.title | ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK BASED MATERNAL AND FETAL HEALTH RISK LEVEL PREDICTION AND SENSITIVITY ANALYSIS DURING PREGNANCY |