Statistical Comparison of Classification Algorithms for Medical Datasets

The objective of this study is to compare the statistical performance of different classification methods on three medical datasets in terms of accuracy, recall, precision and F-score. The comparison is performed by building models using 32 different machine learning-based hybridised techniques and classification algorithms, which yields unbiased, accurate and reproducible results. For this purpose, well-known medical datasets; Wisconsin Breast Cancer dataset, Pima Indians Diabetes dataset and Liver Disorders dataset, are used. Different preprocessing approaches such as missing value imputation, normality test and feature selection are implemented in an open source package R for each dataset. The experimental results indicate an average of 10 runs of 10-fold cross-validation. Promisingly, this comprehensive study analyses the statistical performance of 32 classification techniques on each medical datasets.