A4 Article in conference proceedings
A Reliable Weighted Feature Selection for Auto Medical Diagnosis

List of Authors: Golnaz Sahebi, Amin Majd, Masoumeh Ebrahimi, Juha Plosila, Hannu Tenhunen
Publication year: 2017
Book title *: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
Number of pages: 7
ISBN: 978-1-5386-0838-8
eISBN: 978-1-5386-0837-1
ISSN: 1935-4576


Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed. The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. The accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of 66.76% using all the features.

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Last updated on 2019-16-07 at 18:07