ArticleWorld journal of gastroenterology2022
Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.
Article in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 59 citations in OpenAlex.
- Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Eco friendly deep eutectic solvent based extraction of Raphanus sativus leaf bioactives with mechanistic and antibacterial evaluation.Scientific reports · 2025Article
- Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.medRxiv : the preprint server for health sciences · 2025Article
- The relationship between liver stiffness, fat content measured by liver elastography, and coronary artery disease: a study based on the NHANES database.Scientific reports · 2025Article
- Application of artificial intelligence in portal hypertension and esophagogastric varices.World journal of gastroenterology · 2025Review
- Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study.Neuropsychiatric disease and treatment · 2025Article
- Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare.PloS one · 2025Article
- A novel approach for accurate disease prediction: Application to heart and liver diseases.Journal of education and health promotion · 2025Article
- Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.BMC medical informatics and decision making · 2024Article
- Green synthesis, characterization, and hepatoprotective effect of zinc oxide nanoparticles fromHeliyon · 2024Article
- A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.Frontiers in endocrinology · 2024Article
- Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches.AIMS public health · 2024Article
- An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.PloS one · 2024Article
- An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLiver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.
aimTo present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.
methodsThe dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".
resultsThe results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.
conclusionThis study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.
Indexed as
Identifiers
What OpenQuestion holds
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.