Results 51 to 60 of about 13,919 (263)

Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle

Journal of Applied Informatics and Computing
Heart disease is one of the leading causes of death in the world with risk factors such as atherosclerosis, high blood pressure, and smoking. Early diagnosis is essential to reduce mortality and improve patients' quality of life. This study evaluates the
Gusti Ayu Putu Febriyanti, Anna Baita
doaj   +1 more source

On Generalized Schürmann Entropy Estimators

Entropy, 2022
We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T.
Peter Grassberger
doaj   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source

An oversampling-undersampling strategy for large-scale data linkage

Frontiers in Big Data
Effective record linkage in big data, particularly in imbalanced datasets, is a critical yet highly challenging task due to the inherent complexity involved.
Hossein Hassani, Mohammad Reza Entezarian, Sara Zaeimzadeh, Leila Marvian, Nadejda Komendantova   +4 more
doaj   +1 more source

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Engineering Reports, 2021
This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real‐world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to ...
Dr. Seba Susan, Amitesh Kumar
doaj   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

High‐Resolution Deep Learning Dixon Magnetic Resonance Imaging of the Sacroiliac Joints Is Noninferior to Standard Magnetic Resonance Imaging in Patients With Suspected Axial Spondyloarthritis

Arthritis &Rheumatology, EarlyView.
Objective To compare the multisequence standard magnetic resonance imaging (sMRI) protocol of the sacroiliac joints with a single high‐resolution deep learning–reconstructed Dixon sequence (DL‐Dixon) in patients with suspected axial spondyloarthritis (axSpA). Methods Seventy‐six patients with chronic low back pain and suspected axSpA underwent clinical,
Dominik Deppe, Mariam Koka, Fabian Proft, Sevtap Tugce Ulas, Kay‐Geert Hermann, Denis Poddubnyy, Mikhail Protopopov, Henriette Baum, Hildrun Haibel, Mehrgan Shahryari, Valeria Rios Rodriguez, Jens Vogel‐Claussen, Torsten Diekhoff   +12 more
wiley   +1 more source

Reweighting Scheme for the Calculation of Grand‐Canonical Expectation Values in Quantum Monte Carlo Simulations With a Fermion Sign Problem

Contributions to Plasma Physics, EarlyView.
ABSTRACT Ab initio path integral Monte Carlo (PIMC) simulations constitute the gold standard for the estimation of a broad range of equilibrium properties of a host of interacting quantum many‐body systems spanning a broad range of conditions from ultracold atoms to warm dense quantum plasmas.
Paul Hamann, Jan Vorberger, Tobias Dornheim   +2 more
wiley   +1 more source

Refining diagnostic boundaries and electroclinical profiles of Lennox–Gastaut syndrome through unsupervised clustering

Epilepsia, EarlyView.
Abstract Objective Lennox–Gastaut syndrome (LGS) is a developmental and epileptic encephalopathy defined by polymorphic seizures, intellectual disability (ID), and characteristic electroencephalographic (EEG) patterns. The applicability and biological validity of current electroclinical criteria remain debated.
Emanuele Cerulli Irelli, Marco Perulli, Maria S. Borioni, Anna Falco, Adolfo Mazzeo, Enrico Cocchi, Maria L. Gambardella, Alessandra Morano, Michela Quintiliani, Maria Picilli, Anna T. Giallonardo, Carlo Di Bonaventura, Domenica I. Battaglia   +12 more
wiley   +1 more source

Long‐term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data

Epilepsia, EarlyView.
Abstract Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI using routine clinical data. The objective of this study was to develop and validate a machine learning approach to predict the onset of posttraumatic epilepsy (PTE) over varying time horizons ...
Mustafa Ozmen, Shashank Vadlamani, James J. Gugger, Megan Amuan, Ramon Diaz‐Arrastia, Mary Jo Pugh, Eamonn Kennedy   +6 more
wiley   +1 more source