Comparing classic regression with credit scorecard model for predicting sick building syndrome risk: A machine learning perspective in environmental assessment

Author:

Hosseini Mohammad RezaORCID,Godini HatamORCID,Fouladi-Fard Reza,Ghanami Zeinab,Ghafoory Nassim,Balali Mohammad,Faridan Mohammad

Funder

Alborz University of Medical Sciences

Publisher

Elsevier BV

Reference81 articles.

1. Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic;Prompetchara;Asian Pac. J. Allergy Immunol.,2020

2. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: a systematic review and meta-analysis;Ssentongo;PLoS One,2020

3. Ten questions concerning occupant health in buildings during normal operations and extreme events including the COVID-19 pandemic;Awada;Build. Environ.,2021

4. Assessment of the indoor air quality of Akure, South–West, Nigeria;Francis;Quality of Life,2019

5. Correlation between the prevalence of certain fungi and sick building syndrome;Cooley;Occup. Health Ind. Med.,1999

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3