دانلود مقاله انگلیسی رایگان:مطالعات در مورد استفاده از داده کاوی ، الگوریتم های پیش بینی و یک زبان تبادل جهانی و استنتاج در تجزیه و تحلیل داده های بهداشت اجتماعی اقتصادی - 2019
بلافاصله پس از پرداخت دانلود کنید
دانلود مقاله انگلیسی داده کاوی رایگان
  • Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data
    Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data


    ترجمه فارسی عنوان مقاله:

    مطالعات در مورد استفاده از داده کاوی ، الگوریتم های پیش بینی و یک زبان تبادل جهانی و استنتاج در تجزیه و تحلیل داده های بهداشت اجتماعی اقتصادی


    منبع:

    Sciencedirect - Elsevier - Computers in Biology and Medicine, 112 (2019) 103369: doi:10:1016/j:compbiomed:2019:103369


    نویسنده:

    Barry Robson*, S. Boray


    چکیده انگلیسی:

    While clinical and biomedical information in digital form has been escalating, it is socioeconomic factors that are important determinants of health on the national and global scale. We show how collective use of data mining and prediction algorithms to analyze socioeconomic population health data can stand beside classical correlation analysis in routine data analysis. The underlying theoretical basis is the Dirac notation and algebra that is a scientific standard but unusual outside of the physical sciences, combined with a theory of expected information first developed for analyzing sparse data but still largely confined to bioinformatics. The latter was important here because the records analyzed (which are for US counties and equivalents, not patients) are very few by contemporary data mining standards. The approach is very unlikely to be familiar to socioeconomic researchers, so the theory and the advantages of our inference nets over the Bayes Net are reviewed here, mostly using socioeconomic examples. While our expertise and focus is in regard to novel analytical methods rather than socioeconomics per se, a significant negative (countertrending) relationship between population health and equity was initially surprising, at least to the present authors. This encouraged deeper exploration including that of the relationship between our data mining methods and traditional Pearsons correlation. The latter is susceptible to giving wrong conclusions if a phenomenon called Simpsons paradox applies, so this is also investigated. Also discussed is that, even for very few records, associative data mining can still demand significant computational resources due to a combinatorial explosion.
    Keywords: Population health | Socioeconomic | Data analytics | Data mining | Sparse data | Inference net | Hyperbolic Dirac net | Bayes net | Decision support


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 26
    حجم فایل: 4238 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi