Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments
Diagnosis, being the first step in medical practice, is very crucial for clinical decision making.
This paper investigates state-of-the-art computational intelligence (CI) techniques applied in …
This paper investigates state-of-the-art computational intelligence (CI) techniques applied in …
R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data
Abstract Background and Objective Retrieving meaningful information from high
dimensional dataset is an important and challenging task. Normally, medical dataset suffers …
dimensional dataset is an important and challenging task. Normally, medical dataset suffers …
R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification
Feature selection is one of the trustworthy processes of dimensionality reduction technique
to select a subset of relevant and non-redundant features from large datasets. Ensemble …
to select a subset of relevant and non-redundant features from large datasets. Ensemble …
A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment
Cancer is caused by the un-controlled division of abnormal cells in a body part. Various
cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) …
cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) …
[HTML][HTML] Artificial intelligence-based prediction for cancer-related outcomes in Africa: status and potential refinements
2021 Deep learning Detection of HSIL and LSIL in cervical cancer Phase I-III Internal
0.97[18] 2018 Machine learning Breast cancer staging Phase I, II, IV NIL 0.84[19] 2021 …
0.97[18] 2018 Machine learning Breast cancer staging Phase I, II, IV NIL 0.84[19] 2021 …
Deep learning based breast cancer detection and classification using fuzzy merging techniques
R Krithiga, P Geetha - Machine Vision and Applications, 2020 - Springer
Automatic identification of abnormal and normal cells is a critical step in computer-assisted
pathology, owing to certain heterogeneous characteristics of cancer cells. However …
pathology, owing to certain heterogeneous characteristics of cancer cells. However …
Candidate circulating microRNAs as potential diagnostic and predictive biomarkers for the monitoring of locally advanced breast cancer patients
This study aimed at investigating the expression of candidate microRNAs (miRs), at initial
diagnosis, during neoadjuvant chemotherapy, and after the tumor resection in locally …
diagnosis, during neoadjuvant chemotherapy, and after the tumor resection in locally …
Knowledge granularity based incremental attribute reduction for incomplete decision systems
C Zhang, J Dai, J Chen - International Journal of Machine Learning and …, 2020 - Springer
Attribute reduction is an important application of rough set theory. With the dynamic changes
of data becoming more and more common, traditional attribute reduction, also called static …
of data becoming more and more common, traditional attribute reduction, also called static …
Breast cancer detection model using fuzzy entropy segmentation and ensemble classification
S Vidivelli, SS Devi - Biomedical Signal Processing and Control, 2023 - Elsevier
According to studies, breast cancer is the deadliest disease and the main cause of the
elevated mortality rates among women. The main method for detecting breast cancer is …
elevated mortality rates among women. The main method for detecting breast cancer is …
Evaluation of feature selection techniques for breast cancer risk prediction
This study evaluates several feature ranking techniques together with some classifiers
based on machine learning to identify relevant factors regarding the probability of …
based on machine learning to identify relevant factors regarding the probability of …