Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database

(چکیده مقاله) :
Abstract :

BACKGROUND: Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization, or prediction. ML techniques have been traditionally applied to large, highly dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathologic features. Recently, the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small,highlydimensional database of patients with glioma.
METHODS: We applied 3 ML techniques (artificial neural networks [ANNs], decision trees [DTs], and support vector machines [SVMs]) and classical logistic regression (LR) to a dataset consisting of 76 patients with glioma of all grades. We compared the effect of applying the algorithms to the raw database versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).
RESULTS: Raw input consisted of 21 variables and achievedperformanceofaccuracy/area(C.I.)underthecurveof 70.7%/0.70 (49.9e88.5) for ANN, 68%/0.72 (53.4e90.4) for SVM, 66.7%/0.64 (43.6e85.0) for LR, and 65%/0.70 (51.6e89.5) for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 (62.9e87.9) for ANN,
73.3%/0.74(62.1e87.4)forSVM,69.3%/0.73(60.0e85.8)forLR, and 65.2%/0.63 (49.1e76.9) for DT.
CONCLUSIONS: We demonstrate that these techniques can also be applied to small, highly dimensional datasets. Our ML

(توضیحات تکمیلی) :

(توضیحات تکمیلی) :
Description :

مقاله ISI انگلیسی اصل
سال انتشار : 2019
فایل ISI انگلیسی اصل با فرمت PDF
تعداد forصفحات فایل ISI انگلیسی اصل : 12 صفحه

Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2019 – مقاله ISI/ نویسندگان: Sandip S. Panesar1, Rhett N. D’Souza2, Fang-Cheng Yeh2,3, Juan C. Fernandez-Miranda1
Sent date(تاریخ ارسال) : 1398/04/02  |   6/23/2019
Number of visits(تعداد بازدید): 662
Key words (کلمات کلیدی): Diagnosis - Gliomas - Logistic regression - Machine learning - Neuro-oncology - Prognostication
Number of pages(تعداد صفحات) : 12
نظرات کاربران در مورد این آگهی
در حال حاضر هیچ نظری ثبت نگردیده است .
ارسال پیام

مقالات مرتبط