Multi-Class Model for Fetal Health Classification Using Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Fetal Health Classification dataset is a multi-class modeling situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 2,126 records of features extracted from Cardiotocography exams. Cardiotocograms are a simple and cost accessible option to assess fetal health. The equipment works by sending ultrasound pulses and reading its response, thus highlighting fetal heart rate, fetal movements, uterine contractions, and more. Three expert obstetricians classified the outcomes into three classes: Normal, Suspect, and Pathological.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 91.52% using the training dataset. Furthermore, we selected Bagging Classifier as the final model as it processed the training dataset with a final accuracy score of 93.80%. When we processed the test dataset with the final model, the model achieved an accuracy score of 94.67%.

CONCLUSION: In this iteration, the Bagging Classifier model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Fetal Health Classification Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.