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# To be filled in later with an consistent contribution guide
- Q : Who writes that ?
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# 🕵️ Introduction
**🛠 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 [aicrowd_CRDIO_challenge].(https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_CRDIO_challenge
![](https://www.researchgate.net/profile/Svetlana_Jankovic-Raznatovic/publication/303555090/figure/fig1/AS:457790276804611@1486157115977/Sinusoidal-cardiotocography-CTG-of-the-patient-with-oscillation-amplitude-which-is_W640.jpg)
`Fetal Development` the instructions sets that doesn't make mistakes as they build. Time to look into a problem related to this.<br/>
- Background: A major contributor to under-five mortality is the death of children in the 1st month of life. `Intrapartum complications` are one of the major causes of perinatal mortality. `Fetal cardiotocograph (CTGs)` can be used as a monitoring tool to identify high-risk women during labor.
- Aim: The objective of this study was to study the precision of machine learning algorithm techniques on `CTG` data in identifying high-risk `fetuses`.
Understand with code! Here is [getting started code](https://discourse.aicrowd.com/t/baseline-mnist/2757) 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` on the basis of fetal state.<br/>
To know about given attributes click [here](https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_CRDIO_challenge/blob/master/dataset_info.md).
# 📁 Files
Following files are available in the `resources` section:
- `./data/train.csv` - (`1700` samples) File that should be used for training and validation purposes by the user.
- `./data/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`.
- Sample submission format available at `./data/sample_submission.csv`.
**Make your first submission [here](https://www.aicrowd.com/challenges/crdio-classify-fetal-state/submissions/new) 🚀 !!**
# 🖊 Evaluation Criteria
During evaluation [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) and [Log Loss](http://wiki.fast.ai/index.php/Log_Loss) will be used to test the efficiency of the model where,
<img src="https://latex.codecogs.com/gif.latex?%24%24F1%20%3D%20%7Bprecision%20*%20recall%20%5Cover%20precision%20&plus;%20recall%7D%24%24"/> </br>
<img src="http://latex.codecogs.com/gif.latex?%24%24%20Log%20Loss%20%3D%20-log%20P%28yt%7Cyp%29%20%3D%20-%28yt%20log%28yp%29%20&plus;%20%281%20-%20yt%29%20log%281%20-%20yp%29%29%20%24%24"/>
# 🔗 Links
* 💪 Challenge Page : https://www.aicrowd.com/challenges/crdio-classify-fetal-state
* 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/crdio-classify-fetal-state/discussion
* 🏆 leaderboard : https://www.aicrowd.com/challenges/crdio-classify-fetal-state/leaderboards
# 📱 Contact
- [Shubham Sharma](shubham@ext.aicrowd.com)
# 📚 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](https://www.researchgate.net/publication/303555090_Massive_fetomaternal_hemorrhage_as_a_cause_of_severe_fetal_anemia/figures?lo=1)
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---
challenge_name: aicrowd_CRDIO_challenge
evaluation_repo: git@gitlab.aicrowd.com:aicrowd/practice-challenges/aicrowd_CRDIO_challenge_evaluator.git
data_url: https://s3.wasabisys.com/aicrowd-practice-challenges/public/crdio/v0.1/test_ground_truth.csv
official_baseline: CRDIO_baseline.ipynb
authors:
- name: Shubham Sharma
email: shubham@ext.aicrowd.com
version: '0.1'
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| Index | Attribute-Name | Details |
| ------------- | ------------- | ------------- |
1 |LBE |FHR baseline value (medical expert) (beats per minute) |
2 |LB |FHR baseline (SisPorto) (beats per minute) |
3 |AC |Number of accelerations per second |
4 |FM |Number of fetal movements per second |
5 |UC |Number of uterine contractions per second |
6 |ASTV |percentage of time with abnormal short term variability |
7 |MSTV |mean value of short term variability |
8 |ALTV |percentage of time with abnormal long term variability |
9 |MLTV |mean value of long term variability |
10 |DL |Number of light decelerations per second |
11 |DS |Number of severe decelerations per second |
12 |DP |Number of prolonged decelerations per second |
13 |DR |Number of repetitive decelerations per second |
14 |Width |width of FHR histogram |
15 |Min |minimum of FHR histogram |
16 |Max |maximum of FHR histogram |
17 |Nmax |Number of histogram peaks |
18 |Nzeros |Number of histogram zeros |
19 |Mode |histogram mode |
20 |Mean |histogram mean |
21 |Median |histogram median |
22 |Variance |histogram variance |
23 |Tendency |histogram tendency |
24 |NSP |Normal=1; Suspect=2; Pathologic=3 |
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