• Nzioka Nguku University of Nairobi
Keywords: Diabetes mellitus, Artificial Intelligence, Decision tree, Conceptual Design, KNN, Agile, I-Afya, Regression Analysis, Diabetes Kenya, Support Vector Machine, Reinforcement Learning, and Knowledge Discovery in Databases.


Computational Intelligence approaches have gained increasing popularity given their ability to cope with large amounts of clinical data and uncertain information. The treatment offered for diabetes aims to keep a patients' blood glucose level as normal as possible and to prevent health complications developing later in their life. Researchers and developers have created diabetes applications and systems that already are frequent on various application stores and shelves. Applications running on artificial intelligence (AI) and cognitive computing models offer promise in diabetes care. This is given the fact that diabetes is a global pandemic. An estimated 425 million people worldwide have diabetes, accounting for 12% of the world's health expenditures and yet one in two persons remain undiagnosed and untreated. Type 2 diabetes is driven by the global obesity epidemic and a sedentary lifestyle that overwhelms the body's internal glucose control requiring exogenous insulin. In Kenya alone, diabetes is a leading cause of kidney failure, lower limb amputations and adult-onset blindness. Thus, research on diabetes care using technological (ICT) solutions will continue to dominate the discussion for quite some time.  The early detection of diabetes is of paramount importance. Generally, a physician diagnoses diabetes by evaluating the current test results of a patient or by comparing the patient with other patients who have the same condition. The early detection and screening for individuals with impaired glucose tolerance can help lower risk of developing diabetes and reduce the long-term burden to individuals and health services. For this reason, artificial intelligent systems for diagnosing diabetes have been an item for research for some time. The use of intelligent systems in the Kenyan health care system can help lower the cost of diabetes treatment besides increasing the access and quality of health care provided to diabetic patients. 


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How to Cite
Nguku, N. (2019). I-AFYA: INTELLIGENT SYSTEM FOR THE MANAGEMENT OF DIABETES IN KENYA. Transactions on Machine Learning and Artificial Intelligence, 7(4), 44-55. https://doi.org/10.14738/tmlai.74.6882