Paper Title

Immune system strength prediction using machine learning

Article Identifiers

Registration ID: IJNRD_214389

Published ID: IJNRD2405590

DOI: Click Here to Get

Authors

Prathamesh Naukarkar , Pranav Surkar , Harshavardhan Patil , Kashyap Shahare , Bhairavi Pawar

Keywords

Immunology,AI in healthcare,Machine learning,Strength Prediction,Immune System

Abstract

The immune system protects the body from infections and outside invaders, which is vital for preserving general health and wellbeing. The ability to accurately predict immune system strength can be immensely valuable for healthcarepractitioners in tailoring personalized treatment plans and interventions. This abstract presents a novel approach to predicting immune system strength using machine learningtechniques. By leveraging diverse immunological and clinical data, we aim to develop a predictive model capable of accurately assessing an individual's immune system strength. The proposed model holdsthe potential to revolutionize healthcare by enablingearly detection of immune-related disorders and facilitating targeted interventions to improve immune health.

How To Cite (APA)

Prathamesh Naukarkar, Pranav Surkar, Harshavardhan Patil, Kashyap Shahare, & Bhairavi Pawar (May-2024). Immune system strength prediction using machine learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), f893-f906. https://ijnrd.org/papers/IJNRD2405590.pdf

Issue

Volume 9 Issue 5, May-2024

Pages : f893-f906

Other Publication Details

Paper Reg. ID: IJNRD_214389

Published Paper Id: IJNRD2405590

Downloads: 000121984

Research Area: Biological Science

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405590.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405590

About Publisher

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

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Call For Paper

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

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How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

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