cardio: Cardiotocography

In nlpred: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

Description

Cardiotocography data from UCI machine learning repository. Raw data have been cleaned and an outcome column added that is a binary variable of predicting NSP (described below) = 2.

Details

Data Set Information: 2126 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. Classification was both with respect to a morphologic pattern (A, B, C. ...) and to a fetal state (N, S, P). Therefore the dataset can be used either for 10-class or 3-class experiments.

Attribute Information:

LB - FHR baseline (beats per minute)

AC - # of accelerations per second

FM - # of fetal movements per second

UC - # of uterine contractions per second

DL - # of light decelerations per second

DS - # of severe decelerations per second

DP - # of prolongued decelerations per second

ASTV - percentage of time with abnormal short term variability

MSTV - mean value of short term variability

ALTV - percentage of time with abnormal long term variability

MLTV - mean value of long term variability

Width - width of FHR histogram

Min - minimum of FHR histogram

Max - Maximum of FHR histogram

Nmax - # of histogram peaks

Nzeros - # of histogram zeros

Mode - histogram mode

Mean - histogram mean

Median - histogram median

Variance - histogram variance

Tendency - histogram tendency

CLASS - FHR pattern class code (1 to 10)

NSP - fetal state class code (N=normal; S=suspect; P=pathologic)

Source

https://archive.ics.uci.edu/ml/datasets/Cardiotocography

References

Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318

nlpred documentation built on Feb. 24, 2020, 1:11 a.m.