Diabetes Prediction Using Feature Engineering Approach : Forecasting Diabetes Risk: Unleashing the Power of Feature Engineering and Hybrid Random Forest Algorithm

Language: English

Published by LAP LAMBERT Academic Publishing

6207458702 / 9786207458707

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  • New
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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Softcover

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nach der Bestellung gedruckt Neuware - Printed after ordering - The fundamental notions behind diabetes mellitus and its types, causes, diagnosis, and significance in the prediction of DM. This technical work gives a short introduction to Data Mining and Machine Learning techniques for the prediction of diabetes mellitus. The different methodologies, techniques, and methods involved in predicting diabetes disease. Additionally, current advances in machine learning were highlighted, which have had a substantial influence on the identification and treatment of diabetes. The entire automatic disease prediction system utilizes data collection, noise removal, feature extraction, selection, and classification techniques. The described steps are examined in different authors perceptively to get the better knowledge about particular steps. The class imbalance problem on medical data and applied sampling technique is evaluated with different iteration which denotes the number of sampling process. Adaptive sampling technique is adopted for balancing the class in medical dataset. This improves the performance of the gradient boosting classifier that provides 97.78% accuracy.…

Seller Inventory # 9786207458707

Title
Diabetes Prediction Using Feature Engineering Approach : Forecasting Diabetes Risk: Unleashing the Power of Feature Engineering and Hybrid Random Forest Algorithm
Author
B. Senthil Kumar
Publisher
LAP LAMBERT Academic Publishing
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6207458702
ISBN 13
9786207458707
Item weight
304 grams
Dimensions
220x150x12 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

Item7 to 10 business days5 to 7 business days
First itemUS$ 39.29US$ 50.52
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