Fuzzy Rule and ML Based Hybrid Framework for Diabetes Detection
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This book introduces a hybrid decision-support system that integrates machine learning, fuzzy inference, and rule-based logic to improve the early detection of diabetes. Using clinical parameters such as glucose, BMI, blood pressure, insulin, and age, the model combines statistical learning with expert knowledge to deliver accurate, interpretable, and reliable predictions. Designed for healthcare professionals, researchers, and students, it demonstrates how intelligent systems can bridge the gap between data-driven analytics and real-world clinical decision-making.
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This book introduces a hybrid decision-support system that integrates machine learning, fuzzy inference, and rule-based logic to improve the early detection of diabetes. Using clinical parameters such as glucose, BMI, blood pressure, insulin, and age, the model combines statistical learning with expert knowledge to deliver accurate, interpretable, and reliable predictions. Designed for healthcare professionals, researchers, and students, it demonstrates how intelligent systems can bridge the gap between data-driven analytics and real-world clinical decision-making.
Beschrijving
(2)
Bol
This book introduces a hybrid decision-support system that integrates machine learning, fuzzy inference, and rule-based logic to improve the early detection of diabetes. Using clinical parameters such as glucose, BMI, blood pressure, insulin, and age, the model combines statistical learning with expert knowledge to deliver accurate, interpretable, and reliable predictions. Designed for healthcare professionals, researchers, and students, it demonstrates how intelligent systems can bridge the gap between data-driven analytics and real-world clinical decision-making.
AmazonPages: 152, Paperback, LAP Lambert Academic Publishing
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