Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence  

作　　者：Hedayetul Islam Md.Sadiq Iqbal Muhammad Minoar Hossain 

机构地区：[1]Department of Computer Science and Engineering,Bangladesh University,Bangladesh [2]Department of Computer Science and Engineering,Mawlana Bhashani Science and Technology University,Bangladesh

出　　处：《Intelligent Medicine》2025年第1期54-65,共12页智慧医学(英文)

摘　　要：Objective Hypertension is a critical medical condition that increases the risks of many fatal diseases.Early detection of hypertension can be crucial to lead a healthy life.Machine learning(ML)can be useful for the early prediction of a patient’s likelihood of having a blood pressure abnormality and preventing it.Explainable artificial intelligence(XAI)is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model.This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI.Methods This study utilized the“Blood Pressure Data for Disease Prediction”dataset from Kaggle.Data were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality,chronic kidney disease,and adrenal and thyroid disorders.We have used several ML algorithms(extreme gradient boosting(XGBoost),random forest(RF),support vector machine(SVM),decision tree(DT),and logistic regression(LR))to predict blood pressure abnormality based on patient’s data.Principal component analysis(PCA)and recursive feature elimination(RFE)algorithms were used as feature optimizers.Key outcome metrics included receiver operating characteristic(ROC)curve analysis and accuracy.Additional performance measurement techniques,such as precision,recall,specificity,F1-score,and kappa were calculated to identify the model with the best performance.Moreover,several XAI methods,namely permutation feature importance(PFI),partial dependence plots(PDP),Shapley additive explanations(SHAP),and local interpretable model-agnostic explanations(LIME)were implemented for additional exploration of our best model.Results The combination of RFE and XGBoost provides the most significant results.The results of the study show that the algorithm has an AUC of 0.95,indicating good discriminatory power in detecting abnormal blood pressure.The accuracy,precision,recall,specific

关 键 词：Machine learning Explainable artificial intelligence Principal component analysis Recursive feature elimination Shapley additive explanations 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]