Fetal-Healt

Machine Learning course Project

Overview   |   References   |   Relation   |   Code   |   Team  

☍   Overview

Cardiotocograms (CTGs) are a simple and cost accessible option to assess fetal health, allowing healthcare professionals to take action in order to prevent child and maternal mortality. The equipment itself works by sending ultrasound pulses and reading its response, thus shedding light on fetal heart rate (FHR), fetal movements, uterine contractions and more. This project is aimed to classify the CTG exams in three categories: Normal, Suspect and Pathological.

☍   References

The dataset is available at https://www.kaggle.com/andrewmvd/fetal-health-classification. This dataset is extracted from the work: Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318 (link). Others references are specified in the relation of our work.

☍   Relation and presentation

The relation of this project and the presentation are available in the Relation and presentation folder.

☍   Code

This project is developed in R. All the code is available in the code folder.

☍   Team

⊜   Marco Piazza

• Current Studies: Computer Science Msc Student @ Università degli Studi di Milano-Bicocca (Unimib) ;
• Background: Bachelor degree in Computer Science @ Università degli Studi di Milano-Bicocca (Unimib).

Linkedin profile

⊜   Elisa Cazzaniga

• Current Studies: Computer Science Msc Student @ Università degli Studi di Milano-Bicocca (Unimib) ;
• Background: Bachelor degree in Computer Science @ Università degli Studi di Milano-Bicocca (Unimib).

Linkedin profile