Enhancing generalization in Sickle Cell Disease diagnosis through ensemble methods and feature importance analysis

2025-02-01

Engineering applications of artificial intelligence

AI Summary

This study addresses the limited generalization and insufficient interpretability of existing diagnostic models for sickle cell disease by proposing an ensemble learning approach that integrates Random Forest and Extremely Randomized Trees. The method incorporates image preprocessing, red blood cell segmentation, and morphological feature extraction to construct a highly generalizable and interpretable diagnostic support system. A key feature identification mechanism is designed to reduce model complexity while significantly enhancing performance. Evaluated on a newly curated validation set, the proposed model achieves an F1-score of 90.71% and an SDS-score of 93.33%, outperforming current gradient boosting methods. The authors further promote reproducibility by open-sourcing the code, hyperparameters, and raw results.

Technology Category

• Machine Learning: Ensemble Methods
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Segmentation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Problem

• Sickle Cell Disease
• Generalization
• Ensemble Methods
• Feature Importance
• Diagnosis

Innovation

• ensemble learning
• feature importance analysis
• generalization
• sickle cell disease diagnosis
• interpretable machine learning