Anemia prediction using gene expression programming (GEP) and explainable artificial intelligence approaches - Abdullah, Abdullah; Saddiqi, Hasnain Ahmad; Qasim, Mahnoor; Khitab, Arooba; Khan, Majid; Ahmad, Shakeel (Computers in biology and medicine, 2025)
Anemia being a global health disorder, affecting millions of people, especially pregnant women, children, and the elderly. Proper and timely diagnosis must be ensured to prevent its adverse effects, but the traditional diagnostic methods are very time-consuming, costly, and subject to human mistakes. This research investigates the application of Gene Expression Programming (GEP), a proven technique in machine learning (ML), in predicting anemia. A publicly available dataset on Kaggle was utilized, with clinical parameters including hemoglobin and red blood cell indices. The hyperparameters of the GEP model were best optimized, and the accuracy rate was 99.30 %. To increase the interpretability of the model, Explainable AI methods Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) were used. The findings revealed that hemoglobin levels and gender were the most significant features for predicting anemia. The work brings into limelight the usefulness of ML-based diagnostic solutions in medicine with dependable, automatic, and interpretable models for classifying anemia. •Anemia is a widespread disease characterized by low levels of RBCs or hemoglobin.•Anemia is caused by iron deficiency and poor nutrition, leading to fatigue, weakness, and impaired development.•The GEP model classified anemic and non-anemic patients with 99.30 % accuracy.•Explainable AI methods, namely SHAP and LIME, correctly explained the impact of input parameters on model predictions.•Hemoglobin level was found to be the most influential parameter in the GEP model's decision.•GEP, combined with explainable AI techniques, has the potential to serve as a reliable anemia diagnostic tool in healthcare.