In this project, our team created a machine-learning model to classify the result of the Cardiotocogram test to ensure the well-being of the fetus. The dataset of interest is the fetal health dataset on Kaggle.
Cardiotocography (CTG) is used during pregnancy to monitor fetal well-being and allows early detection of fetal distress, allowing healthcare professionals to take action in order to prevent child and maternal mortality.
The output variable in this dataset is the fetal heath, which consists of 3 classes: Normal, Suspect, and Pathological.
Download link: https://www.kaggle.com/datasets/andrewmvd/fetal-health-classification
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
- Support Vector Machine
- Decision Tree
- Logistic Regression
- Random Forest
- K Nearest Neighbours
Best model accuracy: K Nearest Neighbours (97%)