XGBoost-Liver: An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model
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Abstract
The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alco... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Artificial Intelligence in Healthcare
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References (27)
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1. Implementation of partitional clustering on ILPD dataset to predict liver disorders
2. Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis10.1016/j.cie.2019.106266 ↗
3. A new complement Naïve Bayesian approach for biomedical data classification10.1007/s12652-018-1160-1 ↗
4. Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection10.1016/j.compbiomed.2020.103991 ↗
5. Comparative analysis of machine learning techniques for Indian liver disease patients
6. Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms10.1016/j.imu.2022.101155 ↗
7. PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features10.1186/s12859-024-05917-0 ↗
8. Feature selection and classification systems for chronic disease prediction: a review10.1016/j.eij.2018.03.002 ↗
9. Enhancing sumoylation site prediction: a deep neural network with discriminative features10.3390/life13112153 ↗
10. Deep intelligent predictive model for the identification of diabetes10.3934/math.2023840 ↗
11. Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays10.1093/nar/gkr241 ↗
12. Comprehensive analysis of computational methods for predicting anti-inflammatory peptides10.1007/s11831-024-10078-7 ↗
13. StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning10.1186/s12859-024-05884-6 ↗
14. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale10.1007/s00726-006-0485-9 ↗
15. Optimized feature learning for anti-inflammatory peptide prediction using parallel distributed computing10.3390/app13127059 ↗
16. Deep-piRNA: bi-layered prediction model for piwi-interacting RNA using discriminative features
17. Sequence-based intelligent model for identification of tumor T cell antigens using fusion features10.1109/ACCESS.2024.3481244 ↗
18. AIPs-DeepEnC-GA: predicting anti-inflammatory peptides using embedded evolutionary and sequential feature integration with genetic algorithm based deep ensemble model10.1016/j.chemolab.2024.105239 ↗
19. DeepAIPs-pred: predicting anti-inflammatory peptides using local evolutionary transformation images and structural embedding-based optimal descriptors with self-normalized BiTCNs10.1021/acs.jcim.4c01758 ↗
20. Random forests: from early developments to recent advancements10.1080/21642583.2014.956265 ↗
21. SVM classification: its contents and challenges10.1007/s11766-003-0059-5 ↗
22. kNN algorithm with data-driven k value10.1007/978-3-319-14717-8_39 ↗
23. Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification10.1038/s41598-024-59777-y ↗
24. Prevalence of diabetes, pre-diabetes and associated risk factors: second national diabetes survey of Pakistan (NDSP), 2016–201710.1136/bmjopen-2017-020961 ↗
25. Hard voting meta classifier for disease diagnosis using mean decrease in impurity for tree models10.18488/76.v9i2.3037 ↗
26. Liver disease prediction using machine learning classification techniques
27. Supervised machine learning models for liver disease risk prediction10.3390/computers12010019 ↗