Anemia prediction using gene expression programming (GEP) and explainable artificial intelligence approaches

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...

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Veröffentlicht in:	Computers in biology and medicine Jg. 196; H. Pt B; S. 110856
Hauptverfasser:	Abdullah, Abdullah, Saddiqi, Hasnain Ahmad, Qasim, Mahnoor, Khitab, Arooba, Khan, Majid, Ahmad, Shakeel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.09.2025
Schlagworte:	Adult Anemia - blood Anemia - diagnosis Anemia - genetics Anemia prediction Artificial Intelligence Explainable AI Female Gene Expression Profiling Gene expression programming Hemoglobins - metabolism Humans Internal Medicine Machine Learning Male Middle Aged Other Explainable AI Anemia prediction Machine learning Gene expression programming
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	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.
AbstractList	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 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. 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 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. AbstractAnemia 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.
ArticleNumber	110856
Author	Qasim, Mahnoor Saddiqi, Hasnain Ahmad Khitab, Arooba Ahmad, Shakeel Abdullah, Abdullah Khan, Majid
Author_xml	– sequence: 1 givenname: Abdullah surname: Abdullah fullname: Abdullah, Abdullah email: abdullahicp2022@gmail.com organization: Khyber Medical College, Peshawar, Pakistan – sequence: 2 givenname: Hasnain Ahmad surname: Saddiqi fullname: Saddiqi, Hasnain Ahmad organization: Department of Chemical Engineering, University of Engineering and Technology Peshawar, Pakistan – sequence: 3 givenname: Mahnoor surname: Qasim fullname: Qasim, Mahnoor organization: Khyber Medical College, Peshawar, Pakistan – sequence: 4 givenname: Arooba orcidid: 0009-0000-5394-5639 surname: Khitab fullname: Khitab, Arooba organization: Khyber Medical College, Peshawar, Pakistan – sequence: 5 givenname: Majid surname: Khan fullname: Khan, Majid organization: Southern Illinois University Edwardsville, USA – sequence: 6 givenname: Shakeel surname: Ahmad fullname: Ahmad, Shakeel organization: Saidu Medical College, Swat, Pakistan
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Cites_doi	10.1016/j.chemolab.2019.103886 10.1093/ajcp/80.3.322 10.1016/j.mlwa.2022.100424 10.1016/j.critrevonc.2007.06.006 10.5937/jomb0-31435 10.1016/j.conbuildmat.2016.10.114 10.1016/j.csbj.2024.02.018 10.1007/BF00175355 10.1101/cshperspect.a011866 10.1016/j.conbuildmat.2010.04.011 10.7314/APJCP.2014.15.21.9367 10.21608/kjis.2023.220945.1014 10.3390/math9222970 10.1038/s41598-024-84120-w 10.1371/journal.pone.0125517 10.1111/ejh.12849 10.1016/j.jii.2021.100224 10.1016/j.procs.2024.03.194 10.1016/j.anclin.2015.10.011 10.1111/j.1532-5415.1992.tb02017.x
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Keywords	Explainable AI Anemia prediction Machine learning Gene expression programming
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Snippet	Anemia being a global health disorder, affecting millions of people, especially pregnant women, children, and the elderly. Proper and timely diagnosis must be... AbstractAnemia being a global health disorder, affecting millions of people, especially pregnant women, children, and the elderly. Proper and timely diagnosis...
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SubjectTerms	Adult Anemia - blood Anemia - diagnosis Anemia - genetics Anemia prediction Artificial Intelligence Explainable AI Female Gene Expression Profiling Gene expression programming Hemoglobins - metabolism Humans Internal Medicine Machine Learning Male Middle Aged Other
Title	Anemia prediction using gene expression programming (GEP) and explainable artificial intelligence approaches
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