ArticleScientific reports2022
Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 2025Article
- Blood cancer prediction model based on deep learning technique.Scientific reports · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.Scientific reports · 2024Article
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- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features.Sensors (Basel, Switzerland) · 2023Article
- A systematic hybrid machine learning approach for stress prediction.PeerJ. Computer science · 2023Article
- Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.Frontiers in artificial intelligence · 2023Article
- Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers.Frontiers in bioengineering and biotechnology · 2023Article
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- Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study.Social network analysis and mining · 2022Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
- Multiomics Analysis and Machine Learning-based Identification of Molecular Signatures for Diagnostic Classification in Liver Disease Types Along the Microbiota-gut-liver Axis.Journal of clinical and experimental hepatologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T-test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.