Data for predicting prostate cancer from blood sample measurements.
Format
The prostate_cancer dataset contains two data frames,
prostate_cancer_train and prostate_cancer_test, which can be loaded
together using data(prostate_cancer).
Both data frames contain the following variables:
- cancer
Prostate cancer confirmed by biopsy (1 = yes, 0 = no).
- symptomatic
Indicator of whether the patient has trouble urinating and/or decreased force of urine stream.
- biomarkerk
Concentration of an unspecified biomarker extracted from a blood sample, where k denotes the biomarker number 1-12.
prostate_cancer_train is the training set and prostate_cancer_test is
the test set.
Source
Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Quellec, G., Lamard, M., Conze, P.-H., Massin, P., Cochener, B., Trone, C., Gain, P., Ordonez, R., Abràmoff, M. D., and others. (2014). Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology, 33(3), 231–234.
Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M., Moga, D. C., Quellec, G., and Niemeijer, M. (2013). Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmology, 131(3), 351–357.
Details
The population consists of patients screened for prostate cancer by biopsy. The data are sham data constructed by replicating the structure of the Messidor-2 dataset. Variables have been renamed and the data have been split into a training set and a test set.
References
Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Quellec, G., Lamard, M., Conze, P.-H., Massin, P., Cochener, B., Trone, C., Gain, P., Ordonez, R., Abràmoff, M. D., and others. (2014). Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology, 33(3), 231–234.
Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M., Moga, D. C., Quellec, G., and Niemeijer, M. (2013). Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmology, 131(3), 351–357.