Author(s):
M. Kate, S. Jangam, T. Pitale, S. Patil, S. Garani, Subhrodipto B. Choudhury
Abstract:
Artificial Neural Networks (ANNs) have emerged as a transformative technology in
the field of medical diagnostics, demonstrating significant potential in disease detection. This
study highlights the application of ANNs in identifying complex diseases such as cancer,
diabetes, and cardiovascular disorders. By analysing diverse datasets comprising clinical,
demographic, and imaging data, ANNs effectively detect disease patterns and predict severity,
aiding early diagnosis and treatment planning. Advanced methodologies, including feature
extraction, image analysis, and cross-validation, were employed to optimize model accuracy
and generalizability. Despite achieving high accuracy rates, challenges such as data quality,
interpretability, and computational demands persist, necessitating further research. The
findings underscore the importance of ANNs in modern healthcare, offering a promising
pathway for improved diagnostic precision and patient outcomes while emphasizing the need
for advancements in data management, ethical considerations, and model interpretability.
Pages: 487-493
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