An improved kNN method based on Spearman’s rank correlation for handling medical missing values

Acute chest pain is a common symptom of cardiovascular disease, and its data have important research value. However, the presence of missing value in medical datasets is almost inevitable, which may adversely affect the results of medical data mining. The imputation method plays an important role in the missing value processing method and it can avoid the loss of information due to sample deletion. In this paper, we proposed a new weighted kNN method based on Spearman correlation, named Spearman-Weights kNN(SW-kNN). It is used for missing value imputation in medical datasets that contain a large proportion of samples with missing values. We conducted experiments on the acute chest pain dataset from West China Hospital and compared SW-kNN with seven other methods (Zero, Median, Mean, Random Forest, kNN, NS-kNN, DNNI). The results show that our proposed method has the best performance among all imputation methods in the experiments, which indicates that SW-kNN can successfully handle missing values in medical data.