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 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 3
May-June 2026

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

IOSA-Enhanced Feature Weighting for Lightweight Cardiovascular Risk Classification

Author(s) Mr. Deepak Yashwantrao Bhadane, Prof. Dr. Monika Tripathi Tripathi
Country India
Abstract Cardiovascular diseases remain a profound challenge to global health, accounting for a significant proportion of premature mortality and long-term disability. Recent shifts toward computational and data-driven approaches have enabled new possibilities for early detection and clinical decision support. However, the predictive efficiency of classical machine-learning models is often undermined by redundant, weakly informative or noisy features in clinical datasets. While complex deep-learning frameworks may offer higher predictive capability, they frequently require substantial computational resources, making them less practical for widespread adoption, particularly in resource-constrained environments. This study introduces a simple and efficient approach in which the Improved Owl Search Algorithm (IOSA) is used exclusively for generating feature-importance weights. These weights adjust the influence of each clinical attribute, enabling a lightweight classifier such as Logistic Regression to achieve improved accuracy without additional computational burden. Experimental evaluation shows meaningful enhancement in prediction accuracy and F1-score. The proposed approach is interpretable, scalable, and suitable for early cardiovascular risk assessment in primary-care and remote clinical settings.
Keywords Cardiovascular Disease, Feature Weighting, Improved Owl Search Algorithm, Lightweight Machine Learning, Clinical Prediction Models
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-06
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62675
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 8 Isu 3Cover Page Vol 8 Isu 2Cover Page Vol 8 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 +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals