Disease Detection using Artificial Neural Network

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.