Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The integration of artificial intelligence (AI) into healthcare has revolutionized disease diagnosis, offering enhanced accuracy, efficiency, and scalability. This paper explores comparison of multi-algorithmic framework for AI-driven disease diagnosis, leveraging diverse machine learning (ML) and deep learning (DL) models to handle a wide range of diagnostic challenges. By employing an ensemble of techniques—including decision trees, support vector machines, neural networks, and convolutional architectures—the study demonstrates how combining algorithms can improve diagnostic precision and robustness across various medical conditions. The paper discusses the comparative performance of these methods on benchmark datasets, outlines the pre-processing and feature engineering techniques essential for clinical data, and highlights real-world applications where such hybrid models are currently deployed. Ethical considerations, data privacy, and the potential for integrating such systems into existing healthcare infrastructure are also examined. Ultimately, this research aims to contribute a comprehensive understanding of how multi-algorithmic strategies can shape the future of intelligent, data-driven disease diagnosis.
Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Disease Diagnosis, Multi-Algorithmic Models, Ensemble Learning, Neural Networks, Medical Data Analysis, Diagnostic Systems, Data-Driven Diagnosis
Cite Article:
"A Comparative Analysis of AI Algorithms for Disease Prediction", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b923-b928, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504215.pdf
Downloads:
000393
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator