Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition whose early detection is crucial for improving social and cognitive outcomes. Current diagnostic tools are often costly, subjective, and inaccessible to many clinics. This work presents GazeScan, an image-based analytics framework that identifies ASD from eye-tracking behavior using only standard video input. The system non-invasively performs gaze estimation via a 16-point geometric calibration and transforms gaze trajectories into grayscale scanpath images. These images are classified using a lightweight convolutional neural network. GazeScan was evaluated on the Eye-Tracking Scan Path (ETSP) dataset with five-fold cross-validation, achieving 97.01% accuracy and an AUC of 0.98. The model’s compact architecture enables real-time inference and mobile deployment without specialized hardware. The results obtained highlight the potential of accessible, AI-enabled digital screening tools to support early ASD detection and broader behavioral healthcare delivery. • Identify autism risk using visual scanpath images derived from eye movement patterns. • Leverage deep learning on grayscale scanpaths for efficient behavioral screening. • Visualize user gaze behavior to support early-stage autism diagnosis. • Analyze facial landmarks to generate reliable gaze-based health assessments. • Support mobile diagnostics with lightweight visual analytics from standard video.

This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.

Ischemic Heart Disease (IHD) stands as one of the primary contributors to worldwide deaths, therefore requiring precise and efficient predictive models. Standard machine learning techniques encounter hurdles, including excessive feature dimensions and unbalanced data distribution together with inappropriate feature group choice that negatively affect model effectiveness. The research introduces an optimized feature selection method by employing an Improved Squirrel Search Algorithm (ISSA) to raise the predictive capacity for IHD classification. The ISSA implements adaptive search features to automatically optimize feature selection, through which it maintains important attributes while eliminating redundant information. The selected features are evaluated using a Random Forest classifier, known for its robustness and interpretability in medical prediction tasks. Experimental results on the University of California Irvine (UCI) Heart Disease dataset show that the Improved Squirrel Search Algorithm–Random Forest (ISSA-RF) model achieves a classification accuracy of 98.12 %, outperforming existing feature selection techniques while reducing computational overhead. Bio-inspired optimization proves effective in medical diagnostics through recent research findings that lead to more efficient predictive healthcare models with interpretable properties.

Identifying meaningful patient phenotypes is a cornerstone of data-driven healthcare, enabling risk stratification, resource allocation, and the design of personalized care strategies. Achieving this requires robust analytical methods that can uncover hidden structure in high-dimensional clinical data while ensuring stability and interpretability of results. In this study, we present a machine learning framework for phenotypic clustering that combines partition-based ( k -means) and probabilistic (latent class analysis, LCA) approaches. By comparing subgroup assignments across these complementary methods, the framework provides an internal validation of clustering assignments. Rather than relying on a single method, the framework validates subgroup assignments through cross-method agreement, strengthening confidence in the robustness of the identified phenotypes and their utility for decision support. We apply the proposed framework to patients with chronic kidney disease (CKD) stratified by prior history of acute kidney injury (AKI), illustrating its value in uncovering population-level heterogeneity. While the mechanisms linking AKI to CKD phenotypic patterns remain poorly understood historically, this study investigates CKD trajectories in patients with and without prior AKI and identifies key phenotypic patterns. The analysis revealed consistent phenotypic structures, with over 80% agreement between the two clustering approaches. Distinct phenotypic patterns emerged between the AKI and non-AKI cohorts, with cardiovascular conditions consistently dominating in both groups. These findings demonstrate how stratified clustering can uncover risk signatures that traditional CKD staging systems may overlook. By combining complementary clustering algorithms, the framework strengthens the analytic foundation of phenotyping studies. Moreover, it enables the design of phenotype specific care pathways such as cluster aware monitoring panels and tailored coordination strategies, thus underscoring the broader potential of data-driven analytics to advance personalized medicine and healthcare decision support. • Identify phenotypic patterns in chronic kidney disease using clustering methods. • Compare statistical and machine learning techniques for patient segmentation. • Reveal cardiovascular disease as a dominant phenotype in kidney disease. • Demonstrate the impact of acute kidney injury on disease progression. • Develop a prognosis tool to enhance clinical decision-making.