This project aims to develop a predictive model to classify the health outcomes of fetuses based on cardiotocogram(CTG) features. EDA was conducted then Naive Bayes and multi-layer perceptron (MLP) models were trained and evaluated. This repo contains code files for that EDA, Modeling, and Reporting. The complete report is included as a PDF file, but code file also include written rationale.
- FetalHH_EDA.ipynb - Exploratory analysis code file of the dataset with detailed markdown interpretations.
- FetalHH_Modeling.ipynb - Modeling code file with detailed markdown explainations and evalutation.
- Predicting Fetal Health Outcomes with Machine Learning.pdf - Final Report
The dataset used in this project is publicly available and can be found here. It consists of 2126 samples and 22 features. Each sample represents a CTG examination, and the features include:
LB - FHR baseline (beats per minute)
AC - # of accelerations per second
FM - # of fetal movements per second
UC - # of uterine contractions per second
DL - # of light decelerations per second
DS - # of severe decelerations per second
DP - # of prolongued decelerations per second
ASTV - percentage of time with abnormal short term variability
MSTV - mean value of short term variability
ALTV - percentage of time with abnormal long term variability
MLTV - mean value of long term variability
Width - width of FHR histogram
Min - minimum of FHR histogram
Max - Maximum of FHR histogram
Nmax - # of histogram peaks
Nzeros - # of histogram zeros
Mode - histogram mode
Mean - histogram mean
Median - histogram median
Variance - histogram variance
Tendency - histogram tendency
CLASS - FHR pattern class code (1 to 10)
NSP - fetal state class code (N=normal; S=suspect; P=pathologic)