Machine Learning-Based Classification Algorithms for Predicting Hepatitis C: A Comprehensive Analysis

Clicks: 2
ID: 312820
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #339 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
A disease's accurate diagnosis is one of the most important tasks in the medical field. The most dangerous sickness, which continuously affects a many individuals is hepatitis disease, hence there is a need to automate the disease diagnosis. This study evaluates the diagnostic performance in terms of various parameters and optimization techniques using a range of machine-learning algorithms on a hepatitis dataset. A large dataset that included clinical history, lab test results, and demographic data was used. To get the data ready for analysis, preprocessing techniques such as data cleaning, data discretization, and data normalization were used. The algorithms included in this study are Multi-Layer-Perceptron, Support Vector Machine, Naive Bayes and Random Forest, these algorithms were trained and assessed using metrics including accuracy, recall, precision and F1 score. To minimize overfitting, the model's performance was checked using K-fold cross-validation. ReLU activation function was applied to Multi-Layer-Perceptron for solving the vanishing gradient problem. The classification accuracy scores demonstrate promising outcomes, with SVM scoring 91.86%, NB scoring 89.43%, RF scoring 89.43% and MLP scoring 92.68%. Among all algorithms MLP shows highest frequency.
Reference Key
imported_1777057087_69ebbd3f94445 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anum Munir
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/720
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.