Effect of data preprocessing on ensemble learning for classification in disease diagnosis

In recent years, supervised machine learning methods have increased attention to extracting clinically relevant information from complex health data. Ensemble learning methods enable the establishment of more successful models by training multiple learners jointly to solve the same problem. Herein, we aimed to compare the performance of classification algorithms after data preprocessing to problems such as missing data, class noise, and class imbalance that may be encountered in the datasets used to make an accurate disease diagnosis. To this end, we used random forest and weighted subspace random forest as bagging algorithms while additive logistic regression and gradient boosted machines algorithms were used as boosting algorithms. The performance and running time of the algorithms were also calculated. Our findings indicated that the performance of algorithms increased after data preprocessing and the performance of boosting algorithms yielded higher results than the bagging algorithms. We also observed that the boosting algorithms were the longest-running ones. In conclusion, complementing existing studies, our work highlights the importance and effect of using multiple data preprocessing methods together.