SYMPTOMS BASED DISEASE PREDICTION SYSTEM USING MACHINE LEARNING AND NLP
—Health Diagnosis, Ensemble Learning, Bagging, Boosting, Next.js, MongoDB, Machine Learning, Web Applica tion.
DiagnoCare remains a significant advancement
in preliminary health diagnosis worldwide, requiring accurate
and timely symptom-based assessment to enable early medical
intervention. Although automated machine learning systems have
improved diagnostic analysis, reliable multi-disease classification
remains challenging due to symptom variability and class dis
tribution across 15 disease conditions. Conventional individual
ML algorithms have demonstrated promising results; however,
achieving consistent performance across all disease categories
while maintaining computational efficiency continues to be an
active research problem.
To address these challenges, this study proposes an ensemble
learning-based framework utilizing 6 ML algorithms (2 Bag
ging + 4 Boosting) for fifteen-class disease classification. The
ensemble employs weighted voting to balance model diversity and
prediction confidence, enabling effective feature extraction with
optimized prediction accuracy. The models are trained on a 3000+
sample synthetic dataset and deployed through Next.js 16 fron
tend with MongoDB backend. Experimental results demonstrate
99% accuracy and high prediction reliability across disease
categories. The trained ensemble is further integrated into a full
stack web application to support real-time preliminary health
screening. The findings indicate that the proposed approach
provides a computationally efficient and practically deployable
solution for automated health diagnosis.
Registration ID: IJVRA_701442 Published ID: IJVRA2603470
"SYMPTOMS BASED DISEASE PREDICTION SYSTEM USING MACHINE LEARNING AND NLP", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.596-603, March-2026, Available :https://ijpub.org/ijvra/papers/IJVRA2603470.pdf
Paper Reg. ID: IJVRA_701442
Published Paper Id: IJVRA2603470
Research Area: Science and Technology
Country: Guntur, Andhra Pradesh, India
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
An International UGC CARE JOURNAL PUBLICATION Low Cost (₹599), Scholarly Open Access, Peer-Reviewed, Refereed Journal Impact Factor 9.12 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage, Crossref DOI Member Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJVRA (IJ Publication) Janvi Wave
© 2026 - Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition.
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