A missing data imputation method based on salp swarm algorithm for diabetes disease

Most of the medical datasets suffer from missing data, due to expense some tests or human faults while recording these tests. This issue affects performance machine learning models because values features will be missing. Therefore, there is a need for specific type methods imputing data. In this research, salp swarm algorithm (SSA) used generating and in pain my ass (also known Pima) Indian diabetes disease (PIDD) dataset, proposed called (ISSA). The obtained results showed that classification three different classifiers which are support vector (SVM), K-nearest neighbour (KNN), Naïve Bayesian classifier (NBC) have been enhanced as compared dataset before applying method. Moreover, indicated issa was performed better than statistical imputation techniques such deleting samples with values, replacing zeros, mean, random values.