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🛠 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

You may know that doctors use Sonograms to ‘see’ the fetus, evaluate its health. Did you know, they also use a technique called Cardiotocography to record the fetal heartbeat during the pregnancy?

A large number of infants die even before they are a month old. Cardiotocography(CTG) is widely used to assess fetal wellbeing and identify high-risk fetuses.

For this puzzle, your 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 1 for normal, 2 forsuspect and 3 for pathologic on the basis of fetal state.

## 📁 Files

Following files are available in the resources section:

• train.csv - (1700 samples) This csv contains the features from the cardiotocograph along with the risk state of the featus as [1-3] denoting normal ,suspect and pathologic respectively.

• test.csv - (426 samples) This csv contains the features from the cardiotocograph but not the risk state of the featus.

## 🚀 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 and accuracy will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

The score of only 60% of the test data will be revealed during the competition.

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

## 📚 References

• Source: Marques de SÃ¡, 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

#### Notebooks

 0 [Getting Started Notebook] CRDIO Challange By gauransh_k About 1 year ago 0