[HTML][HTML] Optimized ensemble learning approach with explainable AI for improved heart disease prediction
Recent advances in machine learning (ML) have shown great promise in detecting heart
disease. However, to ensure the clinical adoption of ML models, they must not only be …
disease. However, to ensure the clinical adoption of ML models, they must not only be …
An interpretable machine learning approach for hepatitis b diagnosis
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious
public health problem globally. Substantial efforts have been made to apply machine …
public health problem globally. Substantial efforts have been made to apply machine …
A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis
F Yang, K Wang, L Sun, M Zhai, J Song… - BMC Medical Informatics …, 2022 - Springer
Background Clinical diagnosis based on machine learning usually uses case samples as
training samples, and uses machine learning to construct disease prediction models …
training samples, and uses machine learning to construct disease prediction models …
A synthetic minority oversampling technique based on Gaussian mixture model filtering for imbalanced data classification
Data imbalance is a common phenomenon in machine learning. In the imbalanced data
classification, minority samples are far less than majority samples, which makes it difficult for …
classification, minority samples are far less than majority samples, which makes it difficult for …
[HTML][HTML] FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data
High dimensional and small samples characterize gene expression data and contain a large
number of genes unrelated to disease. Feature selection improves the efficiency of disease …
number of genes unrelated to disease. Feature selection improves the efficiency of disease …
SELF: a stacked-based ensemble learning framework for breast cancer classification
Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung
cancer. During the past few decades, a substantial amount of cancer cases have been …
cancer. During the past few decades, a substantial amount of cancer cases have been …
Classifier ensemble with evolutionary optimisation enforced random projections
T Mo, L Wang, Y Wu, J Huang, W Liu, R Yang… - Expert Systems with …, 2023 - Elsevier
An effective multi-classifier fusion (MCF) system is demanding in the clinical context in terms
of integrating various diagnosis/prognosis predictive models to arrive at a stable and …
of integrating various diagnosis/prognosis predictive models to arrive at a stable and …
A feature engineering-assisted CM technology for SMPS output aluminium electrolytic capacitors (AEC) considering D-ESR-QZ parameters
AB Kareem, JW Hur - Processes, 2022 - mdpi.com
Recent research has seen an interest in the condition monitoring (CM) approach for
aluminium electrolytic capacitors (AEC), which are present in switched-mode power …
aluminium electrolytic capacitors (AEC), which are present in switched-mode power …
Discriminative latent representation harmonization of multicenter medical data
Data harmonization is critical for establishing generalizable model on multicenter medical
data. Traditional data harmonization strategies aim to align data distributions from different …
data. Traditional data harmonization strategies aim to align data distributions from different …
A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis
F Yang, Z Xu, H Wang, L Sun, M Zhai, J Zhang - Plos one, 2024 - journals.plos.org
Background Cancer diagnosis based on machine learning has become a popular
application direction. Support vector machine (SVM), as a classical machine learning …
application direction. Support vector machine (SVM), as a classical machine learning …