Results 131 to 140 of about 32,654 (268)

A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus

Journal of Diabetes Investigation, Volume 17, Issue 4, Page 689-697, April 2026.
ABSTRACT Background Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. Methods The CGM metrics were collected from 259 adults with T1DM.
Naoki Sakane, Yushi Hirota, Akane Yamamoto, Junnosuke Miura, Hiroko Takaike, Sari Hoshina, Masao Toyoda, Nobumichi Saito, Kiminori Hosoda, Masaki Matsubara, Atsuhito Tone, Satoshi Kawashima, Hideaki Sawaki, Tomokazu Matsuda, Masayuki Domichi, Akiko Suganuma, Seiko Sakane, Takashi Murata   +17 more
wiley   +1 more source

Loan Default Prediction Using Machine Learning Algorithms

Journal of Informatics and Web Engineering
Financial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses ...
Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng   +3 more
doaj   +1 more source

An Interpretable Machine Learning Strategy for Antimalarial Drug Discovery with LightGBM and SHAP

Journal of Future Artificial Intelligence and Technologies
Malaria continues to pose a significant global health threat, and the emergence of drug-resistant malaria exacerbates the challenge, underscoring the urgent need for new antimalarial drugs.
T. R. Noviandy, Ghalieb Mutig Idroes, Irsan Hardi   +2 more
semanticscholar   +1 more source

A GIS‐Integrated Active Learning Framework for Crash Severity Classification in Imbalanced Traffic Data

Transactions in GIS, Volume 30, Issue 2, April 2026.
ABSTRACT Classifying urban traffic crash severity remains challenging because severe incidents are underrepresented in highly imbalanced datasets. This challenge is further intensified by spatiotemporal shifts in data distributions, which can degrade model performance over time.
Reza Mohammadi, Mohammad Taleai
wiley   +1 more source

Comparative Analysis of LightGBM and Random Forest for Daily Bitcoin Closing Price Prediction with Ensemble Approach

Journal of Applied Informatics and Computing
This study performs a comparative analysis of the LightGBM and Random Forest algorithms in predicting daily Bitcoin closing prices, with an exploration of an Ensemble approach for potential improvements in accuracy.
Dionisius Nusaca Redegnosis Nolejanduma, Majid Rahardi   +1 more
doaj   +1 more source

Machine Learning Classification of AI‐Generated and Human‐Mapped Buildings in OpenStreetMap Using Morphometric Analysis

Transactions in GIS, Volume 30, Issue 2, April 2026.
ABSTRACT The proliferation of AI‐generated building footprints in OpenStreetMap (OSM) has transformed crowdsourced mapping, yet the geometric characteristics associated with different digitization methods remain poorly understood. This study presents a comprehensive morphometric analysis of more than 9 million building footprints across 15 ...
Abdulkadir Memduhoğlu
wiley   +1 more source

Deriving All‐Hour Aerosol Optical Depth Over China From Automated Visibility Observations

Geophysical Research Letters, Volume 53, Issue 6, 28 March 2026.
Abstract All‐hour aerosol monitoring remains challenging due to limited spatiotemporal coverage of current observational systems. Here we developed a machine‐learning based framework that derives 24‐hr aerosol optical depth (AOD) from automated visibility measurements.
Zhou Yang, Hengheng Zhao, Zhaoliang Zeng, Ke Gui, Xutao Zhang, Nanxuan Shang, Yu Zheng, Lei Li, Huizheng Che   +8 more
wiley   +1 more source

Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics

Geophysical Research Letters, Volume 53, Issue 6, 28 March 2026.
Abstract Ecological Water Replenishment (EWR) is critical for restoring depleted aquifers, yet quantifying its spatiotemporal impacts remains challenging. Leveraging multi‐source data sets and Light Gradient Boosting Machine (LightGBM), this study reconstructs high‐resolution (250 m) groundwater level dynamics in the Yongding River basin, Beijing, and ...
Weican Li, Chengpeng Lu, Jingsi Zhu, Yu Liu, Chengcheng Wu, Shuangyu Liu, Bo Liu, Longcang Shu   +7 more
wiley   +1 more source

Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models

Journal of Asthma and Allergy
Joon Young Choi,1 Chin Kook Rhee2 1Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; 2Department of Internal Medicine, Seoul St.
Choi JY, Rhee CK
doaj  

Impact of lightGBM hyperparameters on class imbalance

Class imbalance is a common problem in Machine Learning (ML) that introduces bias during the training phase of ML models, compromising their accuracy and reliability. This problem is particularly critical in fields such as disease diagnosis and credit risk assessment, where it is crucial to accurately predict the minority class.
openaire   +2 more sources