A comprehensive evaluation of sampling techniques in addressing class imbalance across diverse datasets

MS Mohosheu, MDA al Noman… - 2024 6th International …, 2024‏ - ieeexplore.ieee.org
Class imbalance is a frequently occurring issue in predictive modeling. Learning from
imbalanced data is a challenging task that has attracted much interest from scholars. While a …

ibrf: Improved balanced random forest classifier

A Newaz, MS Mohosheu… - 2024 35th Conference …, 2024‏ - ieeexplore.ieee.org
Class imbalance poses a major challenge in different classification tasks, which is a
frequently occurring scenario in many real-world applications. Data resampling is …

A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification

S Abbas, S Ojo, M Krichen, MA Alamro, A Mihoub… - IEEE …, 2024‏ - ieeexplore.ieee.org
One of the main causes of death from cardiovascular diseases is Myocardial Infarction (MI),
which is brought on by coronary artery problems. Myocardial infarction is a pathological …

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

G MacCarthy, R Pazoki - Journal of Clinical Medicine, 2024‏ - mdpi.com
Background and Objective: Hypertension increases the risk of cardiovascular diseases
(CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global …

iCost: A Novel Instance Complexity Based Cost-Sensitive Learning Framework

A Newaz, AUR Adib, T Jabid - arxiv preprint arxiv:2409.13007, 2024‏ - arxiv.org
Class imbalance in data presents significant challenges for classification tasks. It is fairly
common and requires careful handling to obtain desirable performance. Traditional …

[PDF][PDF] A Machine Learning Based Investigative Analysis for Predicting the Critical Temperature of Superconductors

FA Shams, RH Ratul, AI Naf, SSH Samir… - EAI Endorsed Trans …, 2023‏ - researchgate.net
INTRODUCTION: Ever since the initial discovery of superconductivity, the fundamental
concept and the complex relationship between critical temperature and superconductive …

Feature optimized hybrid model for prediction of myocardial infarction

S Mishra, M Pandey, SS Routaray - F1000Research, 2025‏ - f1000research.com
Background Cardiovascular disease is rampant worldwide and has become the leading
factor in increasing the global mortality rates. According to the World Heart Federation, death …

Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry Data

N ALdausari, F Coenen… - Proceedings of the …, 2024‏ - livrepository.liverpool.ac.uk
Previous research indicated that white blood cell counts and phenotypes can predict
complications after Myocardial Infarction (MI). However, progress is hindered by the need to …

Exploring New Frontiers in Imbalanced Learning: Data Complexity-Based Solutions

A Newaz - 2024‏ - 103.133.167.11
Class imbalance is a frequently occurring scenario in classification tasks. Learning from
imbalanced data poses quite a challenge which has instigated a lot of research in this area …

[PDF][PDF] CLASSIFYING IN-HOSPITAL MYOCARDIAL INFARCTION COMPLICATION MORTALITY

R PEX‏ - arno.uvt.nl
Myocardial Infarction (MI) is one of the most challenging medical emergencies where the
heart muscle begins to die due to a lack of blood flow. About 50% of the patients contract …