Data

plasma_retinol

active
ARFF
Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren

0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

Issue | #Downvotes for this reason | By |
---|

Loading wiki

Help us complete this description
Edit

Author:
Source: Unknown - Date unknown
Please cite:
Determinants of Plasma Retinol and Beta-Carotene Levels
Summary:
Observational studies have suggested that low dietary intake or low plasma concentrations of retinol, beta-carotene, or other carotenoids might be associated with increased risk of developing certain types of cancer. However, relatively few studies have investigated the determinants of plasma concentrations of these micronutrients. We designed a cross-sectional study to investigate the relationship between personal characteristics and dietary factors, and plasma concentrations of retinol, beta-carotene and other carotenoids. Study subjects (N = 315) were patients who had an elective surgical procedure during a three-year period to biopsy or remove a lesion of the lung, colon, breast, skin, ovary or uterus that was found to be non-cancerous. We display the data for only two of the analytes.
Plasma concentrations of the micronutrients varied widely from subject to subject. While plasma retinol levels varied by age and sex, the only dietary predictor was alcohol consumption (R^2 = .38). Plasma beta-carotene levels were log-transformed prior to the analyses due to severe asymmetry of the residuals on the original scale. For log beta-carotene, dietary intake, regular use of vitamins, and intake of fiber were associated with higher plasma concentrations, while Quetelet Index (defined as weight/height^2 in the units kg/m^2) and cholesterol intake were associated with lower plasma levels, adjusting for the other factors (R^2 = .50). There was one extremely high leverage point in alcohol consumption that was deleted prior to the analyses. Plasma concentrations of retinol and beta-carotene were not correlated.
We conclude that there is wide variability in plasma concentrations of these micronutrients in humans, and that much of this variability is associated with dietary habits and personal characteristics. A better understanding of the physiological relationship between some personal characteristics and plasma concentrations of these micronutrients will require further study.
Authorization: Contact Authors
Reference: These data have not been published yet but a related reference is
Nierenberg DW, Stukel TA, Baron JA, Dain BJ, Greenberg ER. Determinants of plasma levels of beta-carotene and retinol. American Journal of Epidemiology 1989;130:511-521.
Description: This datafile contains 315 observations on 14 variables. This data set can be used to demonstrate multiple regression, transformations, categorical variables, outliers, pooled tests of significance and model building strategies.
Variable Names in order from left to right:
AGE: Age (years)
SEX: Sex (1=Male, 2=Female).
SMOKSTAT: Smoking status (1=Never, 2=Former, 3=Current Smoker)
QUETELET: Quetelet (weight/(height^2))
VITUSE: Vitamin Use (1=Yes, fairly often, 2=Yes, not often, 3=No)
CALORIES: Number of calories consumed per day.
FAT: Grams of fat consumed per day.
FIBER: Grams of fiber consumed per day.
ALCOHOL: Number of alcoholic drinks consumed per week.
CHOLESTEROL: Cholesterol consumed (mg per day).
BETADIET: Dietary beta-carotene consumed (mcg per day).
RETDIET: Dietary retinol consumed (mcg per day)
BETAPLASMA: Plasma beta-carotene (ng/ml)
RETPLASMA: Plasma Retinol (ng/ml)
Therese Stukel
Dartmouth Hitchcock Medical Center
One Medical Center Dr.
Lebanon, NH 03756
e-mail: stukel@dartmouth.edu
Information about the dataset
CLASSTYPE: numeric
CLASSINDEX: none specific

RETPLASMA (target) | numeric | 257 unique values 0 missing | |

AGE | numeric | 60 unique values 0 missing | |

SEX | nominal | 2 unique values 0 missing | |

SMOKSTAT | nominal | 3 unique values 0 missing | |

QUETELET | numeric | 258 unique values 0 missing | |

VITUSE | nominal | 3 unique values 0 missing | |

CALORIES | numeric | 311 unique values 0 missing | |

FAT | numeric | 275 unique values 0 missing | |

FIBER | numeric | 157 unique values 0 missing | |

ALCOHOL | numeric | 71 unique values 0 missing | |

CHOLESTEROL | numeric | 304 unique values 0 missing | |

BETADIET | numeric | 302 unique values 0 missing | |

RETDIET | numeric | 278 unique values 0 missing | |

BETAPLASMA | numeric | 212 unique values 0 missing |

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

3.56

Third quartile of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

2.02

First quartile of kurtosis among attributes of the numeric type.

589.29

Third quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.58

Standard deviation of the number of distinct values among attributes of the nominal type.

2.67

Average number of distinct values among the attributes of the nominal type.

1.15

First quartile of skewness among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

12.32

First quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

3.47

Second quartile (Median) of kurtosis among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

189.89

Second quartile (Median) of means among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Minimal mutual information between the nominal attributes and the target attribute.

1.48

Second quartile (Median) of skewness among attributes of the numeric type.

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

131.99

Second quartile (Median) of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

3

The maximum number of distinct values among attributes of the nominal type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

17.21

Third quartile of kurtosis among attributes of the numeric type.