Handling imbalanced medical datasets: review of a decade of research
Abstract Machine learning and medical diagnostic studies often struggle with the issue of
class imbalance in medical datasets, complicating accurate disease prediction and …
class imbalance in medical datasets, complicating accurate disease prediction and …
Two novel SMOTE methods for solving imbalanced classification problems
Y Bao, S Yang - IEEE Access, 2023 - ieeexplore.ieee.org
The imbalanced classification problem has always been one of the important challenges in
neural network and machine learning. As an effective method to deal with imbalanced …
neural network and machine learning. As an effective method to deal with imbalanced …
Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies
Abstract The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline
for solving unbalanced data problems. The working concept of the SMOTE method is to …
for solving unbalanced data problems. The working concept of the SMOTE method is to …
Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study
Purpose Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor
prognosis. Radiomic features have emerged as promising predictors of the tumor …
prognosis. Radiomic features have emerged as promising predictors of the tumor …
[HTML][HTML] Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and …
Simple Summary This research focuses on improving cancer diagnosis and prognosis by
addressing a common problem in data analysis known as class imbalance, where some …
addressing a common problem in data analysis known as class imbalance, where some …
The jeopardy of learning from over-sampled class-imbalanced medical datasets
The usefulness of the oversampling approach to class-imbalanced structured medical
datasets is discussed in this paper. In this regard, we basically look into the oversampling …
datasets is discussed in this paper. In this regard, we basically look into the oversampling …
Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective
M Weaver, DA Goodin, HA Miller, D Karmali… - Scientific Reports, 2024 - nature.com
Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for
early clinical interventions, prevention of secondary complications such as nosocomial …
early clinical interventions, prevention of secondary complications such as nosocomial …
Prediction of depression among women using random oversampling and random forest
LK **n - 2021 International Conference of Women in Data …, 2021 - ieeexplore.ieee.org
Mental Health is one of the significant issues affecting lives, especially during the COVID-19
pandemic. One of the many mental health problems is Depression. According to the latest …
pandemic. One of the many mental health problems is Depression. According to the latest …
Diagnosis of breast cancer on imbalanced dataset using various sampling techniques and machine learning models
R Gupta, R Bhargava… - 2021 14th International …, 2021 - ieeexplore.ieee.org
Breast Cancer is the second most leading cause of death among women. The early
detection of the disease increases the chances of survival of the patient. Therefore, there is …
detection of the disease increases the chances of survival of the patient. Therefore, there is …
Custom machine learning algorithm for large-scale disease screening-taking heart disease data as an example
Heart disease accounts for millions of deaths worldwide annually, representing a major
public health concern. Large-scale heart disease screening can yield significant benefits …
public health concern. Large-scale heart disease screening can yield significant benefits …