Name: Diabetes Prediction Using Feature Engineering Approach (en Inglés)
Price: 111.42 USD
Availability: InStock
Author: Ramasamy, Gunavathi ; Senthil Kumar, B.
ISBN: 9786207458707

portada Diabetes Prediction Using Feature Engineering Approach (en Inglés)

Formato

Libro Físico

Autor

Gunavathi Ramasamy

B. Senthil Kumar

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

192

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 1.1 cm

Peso

0.29 kg.

ISBN13

9786207458707

Categorías

Tecnología: cuestiones generales

Diabetes Prediction Using Feature Engineering Approach (en Inglés)

Gunavathi Ramasamy (Autor) · B. Senthil Kumar (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo

$ 111.42

$ 222.83

Ahorras: $ 111.42

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 19 de Julio y el Martes 30 de Julio.

Lo recibirás en cualquier lugar de Ecuador entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Diabetes Prediction Using Feature Engineering Approach (en Inglés)"

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.