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. 2025 Apr 11;25(1):162.
doi: 10.1186/s12911-025-02874-3.

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models

Olushina Olawale Awe  1 , Peter Njoroge Mwangi  2 , Samuel Kotva Goudoungou  2 , Ruth Victoria Esho  3 , Olanrewaju Samuel Oyejide  4
Affiliations

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models

Olushina Olawale Awe et al. BMC Med Inform Decis Mak. .

Abstract

Background: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great promise in improving the diagnosis of infectious diseases, such as malaria.

Objectives: This study aims to integrate ensemble machine learning models and Explainable Artificial Intelligence (XAI) frameworks to enhance the diagnosis accuracy of malaria.

Methods: The study utilized a dataset from the Federal Polytechnic Ilaro Medical Centre, Ilaro, Ogun State, Nigeria, which includes information from 337 patients aged between 3 and 77 years (180 females and 157 males) over a 4-week period. Ensemble methods, namely Random Forest, AdaBoost, Gradient Boost, XGBoost, and CatBoost, were employed after addressing class imbalance through oversampling techniques. Explainable AI techniques, such as LIME, Shapley Additive Explanations (SHAP) and Permutation Feature Importance, were utilized to enhance transparency and interpretability.

Results: Among the ensemble models, Random Forest demonstrated the highest performance with an ROC AUC score of 0.869, followed closely by CatBoost at 0.787. XGBoost, Gradient Boost, and AdaBoost achieved ROC AUC scores of 0.770, 0.747, and 0.633, respectively. These methods evaluated the influence of different characteristics on the probability of malaria diagnosis, revealing critical features that contribute to prediction outcomes.

Conclusion: By integrating ensemble machine learning models with explainable AI frameworks, the study promoted transparency in decision-making processes, thereby empowering healthcare providers with actionable insights for improved treatment strategies and enhanced patient outcomes, particularly in malaria management.

Keywords: Binary classification; Malaria diagnosis; Nigeria; Prediction; Symptoms.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

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Fig. 1
Research design flowchart. Source: Author
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Correlation matrix of malaria dataset
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Target classes before balancing
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Target classes after oversampling
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Random Forest before balancing
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CatBoost before balancing
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XGBoost before balancing
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AdaBoost before balancing
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GradientBoost before balancing
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Random Forest after oversampling
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CatBoost after oversampling
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XGBoost after oversampling
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AdaBoost after oversampling
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GradientBoost after oversampling
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ROC curve after oversampling
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LIME Random Forest
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LIME CatBoost
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SHAP individual
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SHAP overall
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PFI Random Forest
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PFI CatBoost
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