🛠️ Contribute : Found a typo? Or any other change in the description that you would like to see ? Please consider sending us a pull request in the public repo of the challenge here.

🕵️ Introduction

Can you help save infant lives? A large number of infants die even before they are a month old. Majority of these deaths could be avoided through early diagnosis using monitoring tools such as Fetal cardiotocograph (CTGs). The goal is to develop a machine learning model which can use CTG data for identifying high-risk fetuses.

Understand with code! Here is getting started code for you.😄

💾 Dataset

The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.

These fetal cardiotocograms (CTGs) were automatically processed and the respective diagnostic features measured. The CTGs were also classified by three expert obstetricians and a consensus classification label assigned to each of them. The dataset consists of 24 attributes out of which first 23 attributes describes details of CTGs features and last attribute called NSP is used to classify these CTGs in normal, suspect and pathologic on the basis of fetal state.

To know about given attributes click here.

📁 Files

Following files are available in the resources section:

• train.csv - (1700 samples) File that should be used for training and validation purposes by the user.
• test.csv - (426 samples) File that will be used for actual evaluation for the leaderboard score.

🚀 Submission

• Prepare a csv containing header as NSP and predicted value as digit [1-3] with name as submission.csv.
• Name of the above file should be submission.csv.
• Sample submission format available at sample_submission.csv in the resorces section.

Make your first submission here 🚀 !!

🖊️ Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

🔗 Links

• 💪 Challenge Page: https://www.aicrowd.com/challenges/crdio
• 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/crdio/discussion
• 🏆 Leaderboard: https://www.aicrowd.com/challenges/crdio/leaderboards

📱 Contact

• Shubham Sharma

📚 References

• Source: Marques de Sá, J.P., jpmdesa@gmail.com, Biomedical Engineering Institute, Porto, Portugal. Bernardes, J., joaobern@med.up.pt, Faculty of Medicine, University of Porto, Portugal. Ayres de Campos, D., sisporto@med.up.pt, Faculty of Medicine, University of Porto, Portugal.

• Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

• Image source