Optimal feature selection and detection of sickle cell anemia detection using enhanced whale optimization with clustering based boosted C5.0 algorithm for tribes of Nilgiris

C. Maria Sheeba; K. Sarojini

doi:10.59796/jcst.V13N2.2023.1737

Authors

C. Maria Sheeba Department of Computer Science, L.R.G Government Arts College for Women, Tripur, Tamil Nadu 641604, India
K. Sarojini Department of Computer Science, L.R.G Government Arts College for Women, Tripur, Tamil Nadu 641604, India

DOI:

https://doi.org/10.59796/jcst.V13N2.2023.1737

Keywords:

classification, clustering based boosted C5.0 algorithm, enhanced whale optimization, features selection, sickle cell anemia, sickle cell disease

Abstract

Degradation of red blood cells (RBC) causes many diseases, like sickle cell anemia. Diagnosing this disease takes more time because peripheral blood samples must be examined under the microscope. Since the isolated RBC observation is subjective and high error rate leads to reduce the accuracy, the technology is needed to perform this approach. To fulfil these requirements, optimal feature selection and detection of sickle cell anemia using enhanced whale optimization with clustering based boosted C5.0 algorithm in tribes of Nilgiris is proposed in this manuscript. The input dataset is taken from real data set via non-government organization named NAWA (situated in Kotagiri). The reason behind of Nilgiri region is considered in this work is a collection of sickle cell anemia test results of the tribe people who lives in different areas of Nilgiri region. These images are pre-processed using wavelet packet transform cochlear filter bank method to eradicate the noises and to improve the superiority of image. After that, the features are extracted using force-invariant improved feature extraction method. To select the optimal features, enhanced whale optimization (EWO) algorithm is used. These optimal features are classified utilizing clustering based boosted C5.0 algorithm. The proposed method is activated on PYTHON. The proposed method shows 52.32%, 43.78%, 32.78% and 45.90% higher accuracy compared with the existing methods, such as RGSA-MLP-SCA, CRFA-SVM-SCA, AO-LSTM-SCA and BOA-CNN-SCA.

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