Selection Important Symptoms for Medical Datasets
German, Rusul Abdallah, Maha Abdul-Rahman, Noor T. Mahmood, 2018More than 10 pieces in stock at supplier
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The textbook "Selection Important Symptoms for Medical Datasets" offers a comprehensive examination of selecting optimal features in medical datasets. It discusses the significance of feature selection in medical data application systems and presents an efficient procedure based on Principal Component Analysis (PCA), referred to as the JK method. This method aims to enhance recognition accuracy and enable optimal feature selection. By adjusting a parameter known as variance coverage, a model is created that achieves maximum classification accuracy. The results demonstrate that the proposed method is capable of selecting a compact set of superior features at a very low cost. Extensive experimental comparisons with other methods and various classifiers validate the promising results of the PCA-JK strategy in medical data analysis.
Language | German |
topic | Medicine & Care |
Author | Maha Abdul-Rahman, Noor T. Mahmood, Rusul Abdallah |
Book cover | Paperback |
Year | 2018 |
Item number | 55969594 |
Publisher | Lap Lambert Academic |
Category | Reference books |
Release date | 11.3.2025 |
topic | Medicine & Care |
Language | German |
Author | Maha Abdul-Rahman, Noor T. Mahmood, Rusul Abdallah |
Year | 2018 |
Book cover | Paperback |
Year | 2018 |
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