Abstract
Diabetes Mellitus is a chronic, metabolic disease characterized by elevated blood glucose, which over time has potential to cause catastrophic damage to bodily tissue, namely the heart, blood vessels, kidneys, nerves, and eyes. Despite severe consequences that can occur from untreated diabetes, the Centers for Disease Control (CDC) estimates that in the United States of America (USA), over 21% of people with diabetes are unaware they have it [1]. Testing for diabetes can be costly, and access to the specific laboratory equipment required may not be possible for all those who need it. Alternative methods to detect the presence of diabetes are needed, so patients can be accurately identified and treated. Using a dataset containing 520 patient entries and 16 attributes obtained through survey results (age, visual blurring, delayed healing, etc., nearly all binary), we developed a novel method using fuzzy relation-based feature maps, which are multiplied by weights tuned via Genetic Algorithm, then passed through a Logistic Function and rounded back to binary to predict diabetes diagnosis with a classification accuracy of 96% and an F1 Score of 97.1%.
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O’Grady, K.L., Viaña, J., Cohen, K. (2022). Predicting Diabetes Diagnosis with Binary-To-Fuzzy Extrapolations and Weights Tuned via Genetic Algorithm. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_29
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DOI: https://doi.org/10.1007/978-3-030-82099-2_29
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