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- W3128661025 abstract "With the advancements in the areas of Machine Learning (ML) and Data Mining, data driven medical decision support systems are becoming more and more prevalent. Using these techniques, researchers have been trying to build automatic diagnostic systems, that can learn from historical data and predict the presence of a disease for a new patient. This kind of system can be very helpful as a decision support system for medical practitioners. In our work, we have investigated the area of automatic diabetes prediction, using Random Forest and Gradient Boosting classifiers. Through rigorous experimentations on the Pima Indian Diabetes Dataset (PIDD), we have found that with proper data preprocessing, hyper-parameter tuning and oversampling, these tree based ensemble learners can achieve above 90% accuracy, sensitivity and specificity as well as AUC values above 0.90." @default.
- W3128661025 created "2021-02-15" @default.
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- W3128661025 date "2019-03-19" @default.
- W3128661025 modified "2023-09-28" @default.
- W3128661025 title "Automatic Diabetes Prediction Using Tree Based Ensemble Learners" @default.
- W3128661025 hasPublicationYear "2019" @default.
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