ArticleSensors (Basel, Switzerland)2021
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data.Biomimetics (Basel, Switzerland) · 2025Article
- Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.Scientific reports · 2025Article
- Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data.Healthcare informatics research · 2025Article
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Empirical exploration of whale optimisation algorithm for heart disease prediction.Scientific reports · 2024Article
- CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- Performance analysis of multiple input single layer neural network hardware chip.Multimedia tools and applications · 2023Article
- A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection.PeerJ. Computer science · 2023Article
- Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.Frontiers in immunology · 2023Article
- Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.PloS one · 2023Article
- Effective hybrid feature selection using different bootstrap enhances cancers classification performance.BioData mining · 2022Article
- Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller.Cancers · 2022Article
- Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy.Sensors (Basel, Switzerland) · 2022Article
- A New Framework for Precise Identification of Prostatic Adenocarcinoma.Sensors (Basel, Switzerland) · 2022Article
- Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.Computational and mathematical methods in medicine · 2022Article
- Multiclass feature selection with metaheuristic optimization algorithms: a review.Neural computing & applications · 2022Review
- Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea.Frontiers in medicine · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naïve Bayes, and
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