دانلود مقاله انگلیسی رایگان:مقایسه رگرسیون لجستیک و الگوریتم های یادگیری ماشین در پیش بینی بقا از صدمات مغزی آسیب زا - 2019
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  • Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries
    Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries


    ترجمه فارسی عنوان مقاله:

    مقایسه رگرسیون لجستیک و الگوریتم های یادگیری ماشین در پیش بینی بقا از صدمات مغزی آسیب زا


    منبع:

    Sciencedirect - Elsevier - Journal of Critical Care, 54 (2019) 110-116: doi:10:1016/j:jcrc:2019:08:010


    نویسنده:

    Jin-zhou Feng a,b,1, YuWang a,b,1, Jin Peng a,b,c, Ming-wei Suna, Jun Zeng a,b, Hua Jiang a,b,⁎


    چکیده انگلیسی:

    Purpose: To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study. Materials andmethods: Datawas collected fromSTBI patients admitted to the Sichuan Provincial PeoplesHospital between December 2009 and November 2011. Twenty-two machine learning (ML) models were tested, and their predictive performance compared with logistic regression (LR) model. Receiver operating characteristics (ROC), area under curve (AUC), accuracy, F-score, precision, recall and Decision Curve Analysis (DCA) were used as performance metrics. Results: A total of 117 patientswere enrolled. AUC of all ML models ranged from86.3% to 94%. AUC of LRwas 83%, and accuracy was 88%. The AUC of Cubic SVM, Quadratic SVM and Linear SVM were higher than that of LR. The precision ratio of LR was 95% and recall ratio was 91%, both were lower than most ML models. The F-Score of LR was 0.93, which was only slightly better than that of Linear Discriminant and Quadratic Discriminant. Conclusions: The twenty-twoMLmodels selected have capabilities comparable to classical LR model for outcome prediction in STBI patients. Of these, Cubic SVM, Quadratic SVM, Linear SVM performed significantly better than LR.
    Keywords: Traumatic brain injury | Machine learning | Logistic regression | Survival prediction | Support vector machine | Critical illness


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 7
    حجم فایل: 639 کیلوبایت

    قیمت: رایگان


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