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- W3159762906 abstract "In our study, we at first tried out an array of multifarious feature selection techniques individually from two different healthcare datasets—namely, the Pima Indians diabetic dataset and the Coimbra Breast Cancer one and applied machine learning techniques on the reduced feature set to classify between a diseased and healthy subjects. There is a large percentage of people affected with diabetes and breast cancer all across the world, and early detection plays an instrumental role to improve the survival chances. Thus, we have addressed the concern by building a predictive model that can be used as a potential preliminary biomarker for these diseases. Most of the attributes in the feature set are developed through routine pathological tests, and in most of the under-developed countries, there is acute scarcity to perform a diverse variety of such medical tests owing to financial constraints and access to healthcare facilities. Thus, our paper revolves on identifying a few potential biomarkers, both that include some pathological tests and those without it like that of physiological age of an individual, etc., that would be instrumental in predicting these deadly diseases well in advance before further confirmatory routine checks can be carried out." @default.
- W3159762906 created "2021-05-10" @default.
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- W3159762906 date "2021-01-01" @default.
- W3159762906 modified "2023-10-18" @default.
- W3159762906 title "Performance Analysis of Machine Learning Classifiers on Different Healthcare Datasets" @default.
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- W3159762906 doi "https://doi.org/10.1007/978-981-33-4367-2_11" @default.
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