Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.


Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project.


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This article was results based on the interview phase in the English language 

course of even semester [the academic year 2021-2022] Institut Agama Kristen 

Negeri Ambon. The majority concern is how the students of English courses respond 

during even semester conducted. Moreover, only a few students are encouraged to 

finish their course with moderate achievement, and half of the students are stated on 

lower achievement following unconscious narration to be absent during English 


Please cite the following paper when using this dataset:

N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049



Please cite the following paper when using this dataset:

N. Thakur, “MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkeypox outbreak, findings from analysis of Tweets, and open research questions,” Infect. Dis. Rep., vol. 14, no. 6, pp. 855–883, 2022, DOI:



PopMedNet™ is an open-source application used to facilitate multi-site health data networks. It uses a distributed network design that enables data holders to retain full control of their data. Investigators send questions to data holders for review and response. PopMedNet eliminates the need for assembling patient records in a centralized repository, thus preserving patient privacy and confidentiality.

This Dataset contains sample data using the PCORnet Common Data Model for running the regression tests supplied with PopMedNet™.


Abstract (for details, see

Last Updated On: 
Thu, 01/27/2022 - 18:19
Citation Author(s): 
Ji-Ping Lin

Mother’s Significant Feature (MSF) Dataset has been designed to provide data to researchers working towards woman and child health betterment. MSF dataset records are collected from the Mumbai metropolitan region in Maharashtra, India. Women were interviewed just after childbirth between February 2018 to March 2021. MSF comprise of 450 records with a total of 130 attributes consisting of mother’s features, father’s features and health outcomes. A detailed dataset is created to understand the mother’s features spread across three phases of her reproductive age i.e.