International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Wart Classification Using Logistic Regression: Analysis Based on Data Partitioning, Error Rate Comparison and Feature Selection

Author(s) Siddharth Kumar
Country India
Abstract Accurate classification of cancerous warts is pivotal for effective medical intervention, and logistic regression serves as a promising tool for this purpose. This study delves into the realm of wart classification using logistic regression, with a specific focus on three key aspects: data partitioning, error rate comparison, and feature selection. Logistic regression demonstrates commendable accuracy during training, but an observed disparity between training and testing accuracy prompts a critical examination of potential overfitting. Data partitioning unveils mixed results, enhancing overall testing accuracy while diminishing performance on partitioned datasets, emphasizing the importance of meticulous dataset splitting. Furthermore, the impact of feature selection on the model's performance is explored, underscoring the need for a detailed analysis of influential features. The study concludes by proposing future work, including addressing overfitting through regularization, investigating feature importance, exploring alternative classification algorithms, optimizing accuracy through ensemble methods, and expanding the dataset for enhanced generalization. This research contributes to the advancement of wart classification methodologies, providing insights into logistic regression's application and paving the way for refined diagnostic tools in dermatological practice.
Keywords Warts, Logistic Regression, Malignant, Benign, Cryotherapy
Field Computer > Data / Information
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-30
Cite This Wart Classification Using Logistic Regression: Analysis Based on Data Partitioning, Error Rate Comparison and Feature Selection - Siddharth Kumar - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.11374
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.11374
Short DOI https://doi.org/gtbtdw
View / Download PDF File

Share this


E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com