Global Journal of Technology and Optimization

Darryl ND

Publications

• Research
  Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data
  Author(s): Darryl ND and Rahman MMDarryl ND and Rahman MM
  
  Legacy (and current) medical datasets are rich source of information and knowledge. However, the use of most legacy medical datasets is beset with problems. One of the most often faced is the problem of missing data, often due to oversights in data capture or data entry procedures. Algorithms commonly used in the analysis of data often depend on a complete data set. Missing value imputation offers a solution to this problem. This may result in the generation of synthetic data, with artificially induced missing values, but simply removing the incomplete data records often produces the best classifier results. With legacy data, simply removing the records from the original datasets can significantly reduce the data volume and often affect the class balance of the dataset. A suitable method for missing value imputation is very much needed to produce good quality datasets for better analy.. Read More»
  DOI: 10.4172/2229-8711.S1113

  Abstract PDF