Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.

Scientific reports·2026
Same author

Diabetic foot ulcer classification using an enhanced coordinate attention integrated ConvNext model.

Physical and engineering sciences in medicine·2026
Same author

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Microscopy research and technique·2024
Same author

Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT).

Bioengineering (Basel, Switzerland)·2023
Same author

An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks.

Brain sciences·2023
Same author

A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion.

Diagnostics (Basel, Switzerland)·2023

Related Experiment Video

Updated: Aug 20, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Data mining in predicting liver patients using classification model.

Shubashini Rathina Velu1, Vinayakumar Ravi2, Kayalvily Tabianan3

  • 1Prince Mohammad bin Fahd University, Dhahran, Saudi Arabia.

Health and Technology
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a C4.5 Decision Tree model to predict liver disease from liver function tests, achieving 99.36% accuracy. This data mining approach aids early detection and treatment of liver patients.

Keywords:
Classification modelData miningHealthcareLiver disease

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

177
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Aug 20, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

177
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Medical Informatics
  • Data Mining
  • Machine Learning

Background:

  • General practitioners (GPs) often under-investigate liver function test anomalies.
  • Early detection of liver disease is crucial for effective treatment.
  • Liver function tests evaluate blood enzyme and protein levels for signs of liver disease.

Purpose of the Study:

  • To identify potential liver patients using health screening data.
  • To develop an effective prediction system for early liver disease detection.
  • To aid general practitioners in identifying liver patients through data mining.

Main Methods:

  • Utilized a dataset of 30,691 records with 11 attributes.
  • Applied data pre-processing techniques.
  • Employed classification models, specifically Naïve Bayes and C4.5 Decision Tree, for prediction.

Main Results:

  • The C4.5 Decision Tree model demonstrated superior accuracy over Naïve Bayes.
  • Achieved 99.36% accuracy on the training set and 98.40% on the testing set.
  • The proposed approach showed significant accuracy enhancement compared to existing systems.

Conclusions:

  • A machine learning-based framework can effectively predict liver disease.
  • The C4.5 Decision Tree model provides a robust tool for early liver disease diagnosis.
  • The developed system, with its user interface, can serve as an early diagnosis tool in healthcare.