International Journal of Advance Computational Engineering and Networking (IJACEN)
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Volume-12,Issue-9  ( Sep, 2024 )
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Statistics report
Feb. 2025
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 141
Paper Published : 1672
No. of Authors : 4423
  Journal Paper


Paper Title :
Medical Diagnosis Using Machine Learning

Author :Varshith Addepalli, Bavineni Hoshita, Avuluri Neelima, Nidamanuri Venkata Pavan, Anne Venkata Praveen Krishna

Article Citation :Varshith Addepalli ,Bavineni Hoshita ,Avuluri Neelima ,Nidamanuri Venkata Pavan ,Anne Venkata Praveen Krishna , (2024 ) " Medical Diagnosis Using Machine Learning " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 52-59, Volume-12,Issue-9

Abstract : This research delves into the fascinating realm of Artificial Intelligence (AI) and its impact on medical diagnostics, with a primary aim of improving precision and efficiency in healthcare practices. By diving into the world of advanced machine learning algorithms, our study addresses the urgent need for accurate and swift diagnostic tools. We take a handson approach by integrating diverse patient datasets into state- of-the-art AI models, opening doors to a comprehensive analysis of various medical conditions. The standout results reveal a significant boost in diagnostic accuracy, showcasing how our AI system outshines traditional methods across a spectrum of medical cases. Through meticulous evaluation and validation processes, we establish the reliability of our AI-driven diagnostic approach. The outcomes of this research not only promise better patient outcomes but also hint at resource optimization and a shift in how we traditionally approach diagnostics. In a nutshell, our study brings to light the game-changing potential of AI in medical diagnostics, shedding light on its capability to transform healthcare delivery. As we tread into an era of personalized medicine, this research adds valuable insights into the seamless integration of AI technologies, laying the groundwork for a more precise, efficient, and patientcentered healthcare landscape. Keywords - Medical Diagnosis, Artificial Intelligence (AI), Machine Learning, Natural Language Processor

Type : Research paper

Published : Volume-12,Issue-9


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21248   View Here

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