Biomedical and Health Sciences
This diabetes dataset was collected from 2000 people at the Frankfurt Hospital, Germany. There are eight features in the dataset. Among the 2000 samples, 684 people are Diabetes patients and the rest of them are normal. This dataset is available in the Kaggle repository. The eight features are given below.
i. Pregnancies: To express the Number of pregnancies
ii. Glucose: To express the Glucose level in blood
iii. BloodPressure: To express the Blood pressure measurement
iv. SkinThickness: To express the thickness of the skin
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Data were obtained from the established Clinical Data Repository (CDR) of Yichang Central People's Hospital, and the master index dataset of AKI sample cases was derived by combining the inclusion-exclusion conditions of the study samples with a query of the relevant conditions in the CDR. The data from the CDR and the AKI contain protected health information and are not subject to public sharing. Currently, 500 pieces of data were randomly selected as a sample for public disclosure, details of which are attached to the paper.
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Objective, sensitive, and meaningful disease assessments are critical to support clinical trials and clinical care. Speech changes are one of the earliest and most evident manifestations of cerebellar ataxias. This data set contains features that can be used to train models to identify and quantify clinical signs of ataxic speech. Though raw audio or spectrograms cannot be released due to privacy concerns, this data set contains several OpenSMILE feature sets.
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While resistance training promotes muscle hypertrophy, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle hypertrophy. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength, size and mass in an accessible and portable device.
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Completely contactless user identification using palm images acquired from the mobile and handheld devices is a challenging research problem. This database has been acquired from over 600 different subjects, which is the largest to-date and is also made available in public domain to advance much needed research in this area.
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We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio.
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The prognostic survival dataset, Pancreatic Cancer Survival based on Preoperative Features (PCSPF), was constructed to explore the impact of key preoperative features on prognosis based on the follow-up data of patients with pancreatic cancer at Changhai Hospital, Shanghai, China.
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The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.
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Nasal cytology is a medicine field that focuses on the examination of nasal mucosa cells with the objective of recognizing changes in the epithelium, which is frequently subjected to acute or chronic irritation and inflammation caused by viruses, bacteria, or fungi; in the last decade, nasal cytology is becoming increasingly critical in diagnosing nasal conditions.
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Data content: Raw data of RNA-seq results for rabbits.
Experimental animals and groups: Three months old New Zealand white rabbits were divided into three groups: healthy control group, model control group and model treatment group.
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