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This dataset includes 286 instances described by nine attributes, including categorical features. This is an example of imbalanced data. The goal of corresponding predictive task is to predict the occurrence of breast cancer.

The dataset represents EHR results saved for ten years (1999-2008) in clinical care units at 130 US hospitals and integrated delivery networks. Data includes 101766 observations, a description of the patient's condition at the time of admission, information about the diagnosis, and the number of tests performed.

The dataset contains results for 615 patients, who are blood donors and Hepatitis C patients. Demographic features like age are reported next to laboratory results.

Data for mortality prediction among patients with hepatitis symptoms, including fatigue, anorexia, or big liver. As EHR results, we consider information about albumin and bilirubin level. This Dataset is available mostly for educational purposes and has been employed in machine learning research since the 2000s.

Dataset was collected to detect patients with liver disease. Data comes from Andhra Pradesh in India. This dataset contains information about 583 patients and 11 variables.

This dataset was created by combining 6 different sources. All of them were collected in Australia. The dataset is used to identify prognostic factors in thyroid disease among 30 different features. Among them is information from blood tests but also from the patient's interview.