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`](https://geekymedics.com/how-to-read-a-ctg/) data for 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/>
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.<br/>
To know about given attributes click [here](https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_CRDIO_challenge/blob/master/dataset_info.md).
