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Accidental ignition of energetic compounds by electrostatic discharge poses significant safety risks, highlighting the need for precise prediction of electric spark sensitivity. Here we develop a novel interpretable mach...

Abstract Background Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) is essential for optimizing treatment decisions. This study aimed to deve...

Abstract This paper investigates the extent to which dishonest behavior can be predicted using behavioral personality traits and physiological information. An interpretable machine learning approach, SHapley Additive exP...

Traffic congestion prediction plays a crucial role in mitigating congestion. However, the COVID-19 pandemic and associated government control measures have significantly altered urban travel behavior, increasing the comp...

Abstract Backgrounds Acute primary angle-closure glaucoma (APACG) and chronic primary angle-closure glaucoma (CPACG) exhibit distinct clinical features, yet the molecular mechanisms underlying their differing progression...

Abstract Background Postoperative complications of mediastinal tumors significantly impact patients’ quality of life and long-term outcomes; however, a notable gap in the development of predictive tools for their occur...

ObjectiveTo develop and internally validate an interpretable, non-invasive machine learning framework to predict detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).MethodsThis retrospective c...

BackgroundEarly detection of diabetic retinopathy (DR) remains challenging in primary care, where access to ophthalmic screening is limited. We developed and validated a prediction model using routinely collected health ...

BackgroundFibroblast growth factor receptor (FGFR) inhibitors (FGFRis) benefit selected cholangiocarcinoma (CHOL) patients, yet responses remain heterogeneous and are not fully explained by canonical FGFR alterations. Cl...

Abstract Background This study aims to explore spatial heterogeneity within tumors, establish and validate an interpretable machine learning model combining habitat radiomics and deep learning, and investigate its predic...

Abstract The Fermi surface provides indispensable insights into the electronic structure of materials. Here, we present a robust analysis framework employing an interpretable machine learning approach to investigate Ferm...

Abstract Considering crop yield prediction as critical to optimizing agricultural practices and food security, this question is critical to U.S. agricultural planning and regional food security; relevant research on corn...

"This thesis develops a data-driven pricing framework for Airbnb listings in Amsterdam that predicts booking probability, optimizes nightly prices to maximize expected revenue, and explains each recommendation using inte...

Abstract Despite significant progress in the Prognostics and Health Management (PHM) domain using pattern learning systems from data, machine learning (ML) still faces challenges related to limited generalization and wea...

Timely access to public healthcare is fundamental human rights and a key measure of social equity. In Thailand, transportation barriers especially in rural and underserved areas continue to restrict equitable access to m...

Sevgi Ünal,1 Remzi Gürfidan2 1Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Izmir, Türkiye; 2Database, Network Design and Management, Isparta University of Applied Scie...

ObjectiveDespite the widespread use of immune checkpoint inhibitors (ICIs) improving survival outcomes in non-small cell lung cancer (NSCLC) patients, immune-related adverse events (irAEs) triggered by ICIs have become a...

Abstract Skeletal fluorosis (SF) is a chronic metabolic bone disease resulting from long-term excessive fluoride exposure, affecting millions worldwide. Conventional diagnosis relies on radiographic evidence, which often...

Abstract Background Understanding how the respiratory microbiota matures with age is key to improving poultry health and pathogen surveillance, yet the ecological processes shaping this transition remain elusive. We aime...

Abstract Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities, so it has been widely utilised in the fatigue field. However, most ML methods are inexplicable black-box models tha...

Abstract Background Pediatric sepsis remains a leading cause of global mortality, necessitating early and accurate risk stratification. Traditional scoring systems often fail to capture the rapid, non-linear progression ...

Colorimetric assays offer a low-cost, accessible means of diagnostic testing but often suffer from subjective interpretation and variability caused by inconsistent imaging conditions. To address these challenges, we pres...

ObjectiveCongenital Hypogonadotropic Hypogonadism (CHH) is a rare disease with an extremely low incidence, and the outcomes of fertility induction therapy in CHH patients exhibit significant interindividual heterogeneity...

Abstract Purpose This study aimed to identify the effectiveness of free water MRI (FW-MRI) features for predicting amyloid-beta (Aβ) statuses in Alzheimer’s disease (AD) by constructing diagnostic models using machine...