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 ea...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,specificity,F1-score,and kappa scores were 91.50%,88.64%,92.65%,92.27%,90.83%,and 0.8,respectively.According to the XAI experiment,the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction.Adrenal and thyroid diseases,as well as chronic kidney illness,have an impact on the projections.Existing research backs up this conclusion.Conclusion Compared to previous studies on this dataset,our results would be superior,and the use of XAI shed new light on our model’s prediction.This study would provide new insight into blood pressure detection in the medical profession.展开更多
Background:The kidney is one of the most essential organs in our body,and any problems with it are perilous,hence,the early diagnosis of chronic kidney disease(CKD)is crucial.support vector machine(SVM),a popular mach...Background:The kidney is one of the most essential organs in our body,and any problems with it are perilous,hence,the early diagnosis of chronic kidney disease(CKD)is crucial.support vector machine(SVM),a popular machine learning(ML)technique,is an effective solution for building an early CKD diagnosis system.Nowadays ML techniques like SVM are often combined with upcoming quantum computing technology to improve over classical ML.Methods:This research uses classical SVM(CSVM)and Quantum SVM(QSVM)to develop a CKD diagnosis system and compare the efficiency of the two diagnosis systems.This research performs different preprocessing on a CKD dataset.Based on the analysis and preprocessing,two data optimization approaches principal component analysis(PCA)and singular value decomposition(SVD)are applied to generate two optimized datasets.More-over,classification is done on these two datasets by utilizing both CSVM and QSVM.Findings:The comprehensive analysis of various techniques reveals that PCA outperforms SVD when paired with both CSVM and QSVM.Utilizing PCA,CSVM achieves a remarkable accuracy of 98.75%,while QSVM achieves 87.5%accuracy.In contrast,by utilizing SVD,both CSVM and QSVM achieve relatively lower accuracies,with CSVM achieving 96.25%accuracy and QSVM achieving 60%accuracy.Interpretation:The final assessment of this research confirms that QSVM requires more time in classical experi-mental settings compared to CSVM.Furthermore,the research aims to make it easier to catch CKD early by providing reliable and efficient diagnosis methods.At the same time,it opens the door for trying out new quantum ML ideas in healthcare down the line.展开更多
文摘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,specificity,F1-score,and kappa scores were 91.50%,88.64%,92.65%,92.27%,90.83%,and 0.8,respectively.According to the XAI experiment,the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction.Adrenal and thyroid diseases,as well as chronic kidney illness,have an impact on the projections.Existing research backs up this conclusion.Conclusion Compared to previous studies on this dataset,our results would be superior,and the use of XAI shed new light on our model’s prediction.This study would provide new insight into blood pressure detection in the medical profession.
文摘Background:The kidney is one of the most essential organs in our body,and any problems with it are perilous,hence,the early diagnosis of chronic kidney disease(CKD)is crucial.support vector machine(SVM),a popular machine learning(ML)technique,is an effective solution for building an early CKD diagnosis system.Nowadays ML techniques like SVM are often combined with upcoming quantum computing technology to improve over classical ML.Methods:This research uses classical SVM(CSVM)and Quantum SVM(QSVM)to develop a CKD diagnosis system and compare the efficiency of the two diagnosis systems.This research performs different preprocessing on a CKD dataset.Based on the analysis and preprocessing,two data optimization approaches principal component analysis(PCA)and singular value decomposition(SVD)are applied to generate two optimized datasets.More-over,classification is done on these two datasets by utilizing both CSVM and QSVM.Findings:The comprehensive analysis of various techniques reveals that PCA outperforms SVD when paired with both CSVM and QSVM.Utilizing PCA,CSVM achieves a remarkable accuracy of 98.75%,while QSVM achieves 87.5%accuracy.In contrast,by utilizing SVD,both CSVM and QSVM achieve relatively lower accuracies,with CSVM achieving 96.25%accuracy and QSVM achieving 60%accuracy.Interpretation:The final assessment of this research confirms that QSVM requires more time in classical experi-mental settings compared to CSVM.Furthermore,the research aims to make it easier to catch CKD early by providing reliable and efficient diagnosis methods.At the same time,it opens the door for trying out new quantum ML ideas in healthcare down the line.
