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Keywords = weibo philanthropy

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16 pages, 2708 KiB  
Article
Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning
by Nianjiao Peng, Xinlei Zhou, Ben Niu and Yuanyue Feng
Electronics 2021, 10(2), 143; https://doi.org/10.3390/electronics10020143 - 11 Jan 2021
Cited by 16 | Viewed by 5412
Abstract
The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people [...] Read more.
The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the prediction of medical crowdfunding project fundraising amounts and leave some insights that can be taken into consideration by new researchers and help to produce new management practices. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)
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