ArticleInnovations in systems and software engineering2023
A systematic method for diagnosis of hepatitis disease using machine learning.
Article in Innovations in systems and software engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed.
- Early prediction of in-hospital acute brain injury following cardiac arrest using machine learning: development and external validation.Frontiers in neurology · 2026Article
- A study on the effectiveness of machine learning models for hepatitis prediction.Scientific reports · 2025Article
- A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering.Scientific reports · 2025Article
- Diagnostic challenge of cutis Verticis Gyrata (CVG) in a patient presenting clinical features of Noonan or turner syndrome.Molecular genetics and metabolism reports · 2024Article
- Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.Annals of medicine · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).
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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.