Age
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.
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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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233 Views
Although several databases of handwriting movements have been created so, none of them has been specifically designed for studying the effect of age during ellipse drawing. Ninety subjects voluntarily participated in the database construction. Their age ranged from 19 to 85 years: 30 participants in the range [19, 39] years, 30 in the range [40, 59] and 30 subjects in the range [60, 85]. Twenty-six women (range 19-72 years) and sixty-four men (range 25-85 years) participated.
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87 Views
Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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Any work using this dataset should cite this paper as follows:
Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.
Abstract
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1163 Views
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.
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1011 Views
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673 Views
The Cross-sectional Diabetes Risk survey aims to assess the prevalence of diabetes and its risk factors at the same point in time and also provide a "snapshot" of diseases and risk factors simultaneously for individuals belonging to the western region of the Kingdom of Saudi Arabia (KSA).
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786 Views
Datasets contain survey data of 873 rural poor households in the states of Maharashtra, Odisha, Madhya Pradesh, and Rajasthan.
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729 Views