ArticleComputational intelligence and neuroscience2021
An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning assisted cancer disease prediction from gene expression data using WT-GAN.BMC medical informatics and decision making · 2024Article
- Article
- Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision.Bioengineering (Basel, Switzerland) · 2024Article
- A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems.Biomimetics (Basel, Switzerland) · 2024Article
- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images.Life (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearson's correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV (cross-validation) to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity,
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Registered trials
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