Journal of Computer & Information Technology

Papers

Voulume: 11
Issue: 1
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Article No. 1
Applying Machine Learning Improvements Derived From Diabetes Prediction To Macro Healthcare Systems
ROLAND BABAYEV
Colorado Technical University 4435 North Chestnut Street, Colorado Springs, CO 80907 USA
Abstract :

Gunapati Venkata Krishna (GVK) Emergency Management and Research Institute (EMRI) is based in India and is an entity consisting of a partnership between public and private sectors. The entity responds to 30 million emergency calls and saves a million lives annually by deploying 9000 ambulances and managing 20,000 emergencies daily. Although a large scale healthcare system such as EMRI already leverages business intelligence to help enhance its capabilities, further improvements may be achieved by taking advantage of what is learned from smaller, micro-level research studies associated with machine learning prediction of diseases such as diabetes. Machine learning based diabetes prediction research has leveraged feature reduction methods, ensemble machine learning models, hybrid combinations of supervised and unsupervised learning, ‘white box’ modelling techniques, and more, to help enhance prediction accuracy. By integrating such specific improvements, macro-level BI systems such as EMRI can lower operational and treatment costs, more effectively allocate its human and physical resources, improve patient outcomes through personalization, and more effectively predict disease and complication risks.

Keyword : machine learning, diabetes, business intelligence, healthcare systems
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