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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.
The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.
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
pathologic on the basis of fetal state.
To know about given attributes click here.
Following files are available in the
1700samples) This csv contains the features from the cardiotocograph along with the risk state of the featus as
426samples) This csv contains the features from the cardiotocograph but not the risk state of the featus.
- Prepare a csv containing header as
NSPand predicted value as digit
[1-3]with name as
- Name of the above file should be
- Sample submission format available at
sample_submission.csvin the resorces section.
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
The score of only 60% of the test data will be revealed during the competition.
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Source: Marques de SÃ¡, J.P., firstname.lastname@example.org, Biomedical Engineering Institute, Porto, Portugal. Bernardes, J., email@example.com, Faculty of Medicine, University of Porto, Portugal. Ayres de Campos, D., firstname.lastname@example.org, 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.