Enhancing generalization in Sickle Cell Disease diagnosis through ensemble methods and feature importance analysis
Year: 2024
Type: article
Abstract: This work presents a novel approach for selecting the optimal ensemble-based classification method and features with a primarly focus on achieving generalization, based on the state-of-the-art, to provide diagnostic support for Sickle Cell Disease using peripheral blood smear images of red blood cells. We pre-processed and segmented the microscopic images to ensure the extraction of high-quality features. To ensure the reliability of our proposed system, we conducted an in-depth analysis of interpretability. Leveraging techniques established in the literature, we extracted features from blood cells and employed ensemble machine learning methods to classify their morphology. Furthermore, we have devised a methodology to identify the most critical features for classification, aimed at reducing complexity and training time and enhancing interpretability in opaque models. Lastly, we validated our results using a new dataset, where our model overperformed state-of-the-art models in terms of generalization. The results of classifier ensembled of Random Forest and Extra Trees classifier achieved an harmonic mean of precision and recall (F1-score) of 90.71% and a Sickle Cell Disease diagnosis support score (SDS-score) of 93.33%. These results demonstrate notable enhancement from previous ones with Gradient Boosting classifier (F1-score 87.32% and SDS-score 89.51%). To foster scientific progress, we have made available the parameters for each model, the implemented code library, and the confusion matrices with the raw data.
Institution: Universitat de les Illes Balears
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,270
Award: G8769443266
OpenAlex ID: W4405838649