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• W4384347782 abstract "The healthcare industry greatly benefits from the use of data mining techniques. Data volume rises as medical record systems standardize, with much of it going unanalyzed. The study’s goal is to identify the factors that contribute to the prevalence of diabetes while taking into consideration those factors. For the examination of the diabetes database in this study, WEKA, an open-source data mining tool, is employed. To categorize the data, classification algorithms are used. The data is then tested using 10-fold cross-validation, and the outcomes are compared. A target variable called Outcome and several medical predictor factors make up the datasets. The number of pregnancies the patient has had, their BMI, insulin level, age, and other factors are predictor variables. This dataset was obtained from the National Institute of Diabetes and Digestive and Kidney Diseases. The dataset aims to diagnostically predict whether or not a patient has diabetes based on particular diagnostic metrics available in the information. There were some restrictions on how these instances were chosen from a larger database. In particular, Pima Indian women who are at least 21 years old make up every patient in this facility. In this study, we used a variety of classifiers in the Python and Weka programming environments for machine learning to predict the outcome after classification while comparing the classifiers’ levels of accuracy confidence." @default.
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• W4384347782 date "2022-12-01" @default.
• W4384347782 modified "2023-10-18" @default.
• W4384347782 title "Comparative Analysis of Machine Learning and Data Mining based Multi-Models for Diabetes Risk Prediction" @default.
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• W4384347782 doi "https://doi.org/10.1109/icwite57052.2022.10176232" @default.
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