Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
Knowledge discovery in medicine: Current issue and future trend
N Esfandiari, MR Babavalian, AME Moghadam… - Expert Systems with …, 2014 - Elsevier
Data mining is a powerful method to extract knowledge from data. Raw data faces various
challenges that make traditional method improper for knowledge extraction. Data mining is …
challenges that make traditional method improper for knowledge extraction. Data mining is …
Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges …
This study aims to systematically review prior research on the evaluation and benchmarking
of automated acute leukaemia classification tasks. The review depends on three reliable …
of automated acute leukaemia classification tasks. The review depends on three reliable …
Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification
Background Microarray technology is widely used in cancer diagnosis. Successfully
identifying gene biomarkers will significantly help to classify different cancer types and …
identifying gene biomarkers will significantly help to classify different cancer types and …
[HTML][HTML] Gene selection for tumor classification using neighborhood rough sets and entropy measures
With the development of bioinformatics, tumor classification from gene expression data
becomes an important useful technology for cancer diagnosis. Since a gene expression …
becomes an important useful technology for cancer diagnosis. Since a gene expression …
A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification
ZY Algamal, MH Lee - Advances in data analysis and classification, 2019 - Springer
The common issues of high-dimensional gene expression data are that many of the genes
may not be relevant, and there exists a high correlation among genes. Gene selection has …
may not be relevant, and there exists a high correlation among genes. Gene selection has …
A hybrid feature selection method for DNA microarray data
LY Chuang, CH Yang, KC Wu, CH Yang - Computers in biology and …, 2011 - Elsevier
Gene expression profiles, which represent the state of a cell at a molecular level, have great
potential as a medical diagnosis tool. In cancer classification, available training data sets are …
potential as a medical diagnosis tool. In cancer classification, available training data sets are …
A novel hybrid classification model of artificial neural networks and multiple linear regression models
The classification problem of assigning several observations into different disjoint groups
plays an important role in business decision making and many other areas. Developing …
plays an important role in business decision making and many other areas. Developing …
Attribute reduction based on max-decision neighborhood rough set model
X Fan, W Zhao, C Wang, Y Huang - Knowledge-Based Systems, 2018 - Elsevier
The neighborhood rough set model only focuses on the consistent samples whose
neighborhoods are completely contained in some decision classes, and ignores the …
neighborhoods are completely contained in some decision classes, and ignores the …
A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open …
Context Acute leukaemia diagnosis is a field requiring automated solutions, tools and
methods and the ability to facilitate early detection and even prediction. Many studies have …
methods and the ability to facilitate early detection and even prediction. Many studies have …