Key result
Deep neural networks predict hypertension risk with ~12-point higher accuracy than standard machine learning models.
Why the study?
Hypertension is a major cardiovascular risk factor, and early identification of patients at risk can support timely prevention and clinical assessment.
Do machine learning, deep learning, and quantum neural network models improve the prediction and classification of hypertension risk compared to traditional statistical logistic regression in adult clinical patients?
Cross-Sectional (n=624)
No
Do machine learning, deep learning, and quantum neural network models improve the prediction and classification of hypertension risk compared to traditional statistical logistic regression in adult clinical patients?
Deep neural network and exploratory quantum neural network models demonstrated high internal cross-validation accuracy and discrimination for classifying hypertension using clinical and cardiovascular risk factors in a Pakistani hospital cohort.
Answers are grounded in this paper and cite their sources.
ML models may aid hypertension risk stratification if externally validated; leaves open clinical adoption pending prospective trials.
Hypertension is a major public health problem and an important risk factor for cardiovascular disease. Early identification of patients at risk of hypertension can support timely prevention and clinical assessment. This study developed and compared statistical, machine learning, deep learning, and quantum neural network (QNN) models for hypertension prediction using clinical data from 624 patients at District Headquarters Hospital, Faisalabad, Pakistan. The proposed framework involved data preprocessing, K-nearest neighbor imputation, chi-square-based feature selection, feature scaling, and synthetic minority over-sampling technique (SMOTE) for class balancing. Logistic regression was used as a statistical reference, while SVM, random forest, XGBoost, Naive Bayes, ANN, SVM with PCA, DNN, and QNN were evaluated. Stratified 10-fold cross-validation was used to assess model performance using accuracy, F-measure, AUC, precision, recall, specificity, and maximum probability of correct decision (MPCD). The DNN model achieved the highest accuracy (95.77%), while the QNN achieved the highest AUC (95.40%), precision (96.26%), and recall (94.76%). Naive Bayes showed the lowest accuracy (83.40%). The QNN was included as an exploratory hybrid quantum-classical approach using a simulated quantum circuit, and the results do not imply a quantum advantage over classical models. Overall, the evaluated models showed promising predictive performance for hypertension classification in the study population. Further evaluation using independent populations, external validation, and prospective clinical studies is needed before considering their use in routine clinical practice.
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Aslam et al. (2026) conducted a cross-sectional in Hypertension (n=624). Deep Neural Network (DNN) and Quantum Neural Network (QNN) hypertension risk prediction models vs. Classical machine learning and statistical models (Logistic Regression, SVM, Random Forest, XGBoost, Naive Bayes, ANN, SVM+PCA) was evaluated on Hypertension classification accuracy. Deep neural network and quantum neural network models achieved 95.77% accuracy and 95.40% AUC, respectively, for predicting hypertension risk compared to 83.40% accuracy with Naive Bayes.
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