# Enhancing Generalization in Sickle Cell Disease Diagnosis through Ensemble Methods and Feature Importance Analysis

arXiv: https://arxiv.org/abs/2601.13021 · HTML: https://mlsanity.com/paper/2601.13021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images](https://mlsanity.com/paper/2010.04511/) (2020)
- [Diabetic Retinopathy Detection using Ensemble Machine Learning](https://mlsanity.com/paper/2106.12545/) (2021)

## Abstract (arXiv)

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.