AI-Driven Clinical Decision Support Framework for Early Diagnosis of Genetic Diseases In Children
Keywords:
Keywords: Eye pupillometry, Disease prediction, XGBoost model, SVM classification, Machine learning in healthcareAbstract
Eye pupillometry has emerged as a significant biomarker in diagnosing neurological and systemicdiseases by analyzing variations in pupil response to stimuli. In India, early detection of suchconditions remains a challenge due to reliance on traditional diagnostic systems that are manual, timeconsuming, and often limited by human error
References
Agency for Healthcare Research and Quality. Clinical Decision Support.
https://www.ahrq.gov/cpi/about/otherwebsites/clinical-decision-support/index.html.
Manyika, J. et al. Big Data: The Next Frontier for Innovation, Competition, and Productivity.
(2011).
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Published
2025-04-08
How to Cite
R. Deepika, Challa Sandeep Reddy, Lingampally Anjali, Peddagoni Ravi Teja, Cholleti Dhakshayani. (2025). AI-Driven Clinical Decision Support Framework for Early Diagnosis of Genetic Diseases In Children. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 415–427. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2314
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