A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records
Background Supervised machine learning models have been widely used to predict and get
insight into diseases by classifying patients based on personal health records. However, a …
insight into diseases by classifying patients based on personal health records. However, a …
FIUS: Fixed partitioning undersampling method
Abstract Background and Objective In the medical field, data techniques for prediction and
finding patterns of prevalent diseases are of increasing interest. Classification is one of the …
finding patterns of prevalent diseases are of increasing interest. Classification is one of the …
Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Background There is currently no consensus on the impact of class imbalance methods on
the performance of clinical prediction models. We aimed to empirically investigate the impact …
the performance of clinical prediction models. We aimed to empirically investigate the impact …
Predicting disease risks from highly imbalanced data using random forest
M Khalilia, S Chakraborty, M Popescu - BMC medical informatics and …, 2011 - Springer
Background We present a method utilizing Healthcare Cost and Utilization Project (HCUP)
dataset for predicting disease risk of individuals based on their medical diagnosis history …
dataset for predicting disease risk of individuals based on their medical diagnosis history …
Over-and under-sampling approach for extremely imbalanced and small minority data problem in health record analysis
K Fujiwara, Y Huang, K Hori, K Nishioji… - Frontiers in public …, 2020 - frontiersin.org
A considerable amount of health record (HR) data has been stored due to recent advances
in the digitalization of medical systems. However, it is not always easy to analyze HR data …
in the digitalization of medical systems. However, it is not always easy to analyze HR data …
Review of Machine Learning Techniques For Class Imbalance Medical Data Set
Data imbalance threatens a medical dataset where the dominant class is typically viewed as
unfavorable. In contrast, the minority class is t supposed to be the positive one, affecting the …
unfavorable. In contrast, the minority class is t supposed to be the positive one, affecting the …
An evaluation of deep learning with class imbalanced big data
JM Johnson - 2019 - search.proquest.com
Effective classification with imbalanced data is an important area of research, as high class
imbalance is naturally inherent in many real-world applications, eg anomaly detection …
imbalance is naturally inherent in many real-world applications, eg anomaly detection …
Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods
S Sadeghi, D Khalili, A Ramezankhani… - BMC Medical Informatics …, 2022 - Springer
Background Early detection and prediction of type two diabetes mellitus incidence by
baseline measurements could reduce associated complications in the future. The low …
baseline measurements could reduce associated complications in the future. The low …
[HTML][HTML] Machine learning from real data: A mental health registry case study
Imbalanced datasets can impair the learning performance of many Machine Learning
techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are …
techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are …
A novel early diagnostic framework for chronic diseases with class imbalance
Chronic diseases are one of the most severe health issues in the world, due to their terrible
clinical presentations such as long onset cycle, insidious symptoms, and various …
clinical presentations such as long onset cycle, insidious symptoms, and various …