R/dataset.spect.R

In imputeR: A General Multivariate Imputation Framework

#' SPECT Heart Data Set 
#' 
#' The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images
#' Each of the patients is classified into two categories: normal and abnormal. 
#' The database of 267 SPECT image sets (patients) was processed to extract features 
#' that summarize the original SPECT images. As a result, 44 continuous feature pattern
#' was created for each patient. The pattern was further processed to obtain 22 binary feature patterns. 
#' The CLIP3 algorithm was used to generate classification rules from these patterns. 
#' The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardilogists' diagnoses). 
#' SPECT is a good data set for testing ML algorithms; it has 267 instances that are descibed by 23 binary attributes.
#' In the imputation study, it can be treated as a categorical-only data. For detailed information, please refer to
#' the Source and the Reference
#' 
#' \itemize{
#'  \item X1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) 
#'  \item X0. F1: 0,1 (the partial diagnosis 1, binary) 
#'  \item ...
#'  }
#' @source \url{http://archive.ics.uci.edu/ml/datasets/SPECT+Heart}
#' @references Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S. 2001
#' Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis 
#' \emph{Artificial Intelligence in Medicine}, vol. 23:2, pp 149-169
#' @docType data
#' @keywords datasets
#' @format A data frame with 266 rows and 23 variables
#' @name spect
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