Yinhao Li

Hypertension is a leading contributor to cardiovascular morbidity worldwide. When blood pressure and routine clinical variables are available during clinic visits or health checks, guideline thresholds support binary classification but do not necessarily provide reliable calibrated probabilities for decision support. Machine learning studies in hypertension have concentrated on predicting future disease incidence rather than binary classification at the time of measurement, and calibration and validation design are reported inconsistently across this literature. This study examines whether machine learning, trained under a stratified protocol with held‐out calibration and test subsets, can improve probabilistic reliability in post‐measurement classification while remaining comparable to a clinical blood pressure rule. We analysed a public hypertension dataset of 4240 records with a positive‐class prevalence of 31.1% using a scikit‐learn pipeline that integrates preprocessing, recursive feature elimination, Optuna‐based hyperparameter tuning, and isotonic calibration on a held‐out calibration set. A tuned decision tree was compared against a clinical rule based on systolic blood pressure of at least 140 mmHg or diastolic blood pressure of at least 90 mmHg across 30 stratified random splits. Accuracy and recall were comparable to the clinical rule, with no statistically significant Wilcoxon differences, whereas Brier score and expected calibration error were significantly lower for the machine learning model, with mean values of 0.081 versus 0.090 and 0.027 versus 0.084. Interpretability analysis was consistent with blood‐pressure‐related predictors dominating model attributions. These findings suggest that machine learning classification after blood pressure measurement aligns with guideline‐based decisions while offering better calibrated probabilities for clinical decision support.

Medical image segmentation is a crucial task of medical image analysis and computer vision. Medical images, compared to natural ones, contain more complex semantic information, making feature learning more challenging. Existing encoder-decoder architectures are limited by inadequate cross-scale interaction and insufficient boundary modeling in their feature fusion designs. To address this, we propose a Hierarchical Feature Interaction network with Boundary guidance (HFIBNet), which unifies dynamic cross-level feature fusion and explicit edge supervision within a coarse-to-fine segmentation framework. Specifically, we introduce a Boundary Prediction (BP) module to extract boundary-aware features that guide the fusion process. A Cross-Level Feature Fusion (CLFF) module is designed to promote semantic interaction across adjacent encoder stages, while the Edge Feature Aggregation (EFA) module propagates boundary cues hierarchically to enhance structural consistency. Furthermore, a Partially Parallel Decoder (PPD) generates a coarse global prediction, which is progressively refined by a Global-Local Feature Enrichment (GLFE) module, mimicking the clinical annotation workflow from coarse localization to fine delineation. Extensive experiments on ten public medical segmentation datasets across four distinct tasks demonstrate that HFIBNet consistently outperforms existing state-of-the-art methods. The code is available available at https://github.com/ukeLin/HFIBNet.