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- W4367011873 abstract "One of the most prevalence diseases in developing countries in recent years is still Malaria with global public health concern. Malaria is caused by mosquitoe’s parasites which are very common in developing nations. This reason was not far from environmental insanity coupled with inadequate healthcare facilities. Thus, develop a reliable and efficient classification model for early diagnosis and discovery of the malaria symptoms is required. Hence, to reduce malaria endemic globally, the use machine learning models is essentials and paramount. Therefore, this paper proposes malaria diagnosis model using hybrid Support Vector Machines (SVM) and Adaboost. SVM classifiers for malaria classification and eliminating redundant or extraneous features were extracted using Chi-square. The classification accuracy of SVM and Adaboost models gave 97% with the six features removed but from seventh feature the accuracy reduced to 91%. The study revealed that, the developed hybridized model gives optimal solutions by using the most prominent features (symptoms) for malaria classification." @default.
- W4367011873 created "2023-04-27" @default.
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- W4367011873 date "2023-01-01" @default.
- W4367011873 modified "2023-10-16" @default.
- W4367011873 title "Hybridized Support Vector Machine and Adaboost Technique for Malaria Diagnosis" @default.
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- W4367011873 doi "https://doi.org/10.1007/978-981-19-5191-6_3" @default.
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