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 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

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

Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations

Author(s) Soumit Roy, Mainak Mitra
Country India
Abstract In this complex strings of healthcare supply chain, optimal efficiency is the most supreme to ensure cost and time effectiveness of medical resources. This paper scrabbles through Machine Learning (ML) classifiers such as, Na¨ıve Bayes, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM) and Linear Regression to intensify the performance of healthcare supply chain management. This study focuses on five classes: ’Inspection Results’, ’Defect Rate’, ’Transportation Modes’, ’Routes’ and ’Cost’. According to my findings, Random Forest classifier indicated 87% accuracy in the ‘Inspection Results’ and ‘Transportation Modes’ classifications whereas KNN classifier signified an impressive accuracy of 86% in the ‘Routes’ classification. These findings underline the need for ML approaches within different classes. The diversified performances of classifiers within classes highlight the importance of selecting the most suitable algorithm based on supply chain aspect. This research not only shows the effectiveness of ML classifiers in healthcare supply chain optimization but also highlights the possibility of automation for different surface of supply chain management. It provides as a fundamental step for optimizing healthcare supply chain
Keywords Machine Learning, Classification, Supply Chain, Healthcare
Field Engineering
Published In Volume 3, Issue 6, November-December 2021
Published On 2021-12-22
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 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

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