Predicting disease risks from highly imbalanced data using random forest

被引：0
|
作者：
Mohammed Khalilia
Sounak Chakraborty
Mihail Popescu
机构：
[1] Department of Computer Science,Department of Health Management and Informatics
[2] University of Missouri,undefined
[3] Department of Statistics,undefined
[4] University of Missouri,undefined
[5] University of Missouri,undefined
来源：
BMC Medical Informatics and Decision Making | / 11卷
关键词：
Support Vector Machine; Random Forest; Imbalanced Data; Disease Prediction; National Inpatient Sample;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [41] Multi-class random forest model to classify wastewater treatment imbalanced data
    Distefano, Veronica
    Palma, Monica
    De Iaco, Sandra
    SOCIO-ECONOMIC PLANNING SCIENCES, 2024, 95
  • [42] Performance enrichment through parameter tuning of random forest classification for imbalanced data applications
    More, Anjali S.
    Rana, Dipti P.
    MATERIALS TODAY-PROCEEDINGS, 2022, 56 : 3585 - 3593
  • [43] Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data
    Adepu, Anil Kumar
    Sahayam, Subin
    Jayaraman, Umarani
    Arramraju, Rashmika
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 154
  • [44] In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner
    Chen, Jing
    Tang, Yuan Yan
    Fang, Bin
    Guo, Chang
    JOURNAL OF MOLECULAR GRAPHICS & MODELLING, 2012, 35 : 21 - 27
  • [45] Random forest prediction of Alzheimer's disease using pairwise selection from time series data
    Moore, P. J.
    Lyons, T. J.
    Gallacher, J.
    Weiner, Michael W.
    Aisen, Paul
    Petersen, Ronald
    Jack, Clifford R., Jr.
    Jagust, William
    Trojanowki, John Q.
    Toga, Arthur W.
    Beckett, Laurel
    Green, Robert C.
    Saykin, Andrew J.
    Morris, John
    Shaw, Leslie M.
    Khachaturian, Zaven
    Sorensen, Greg
    Carrillo, Maria
    Kuller, Lew
    Raichle, Marc
    Paul, Steven
    Davies, Peter
    Fillit, Howard
    Hefti, Franz
    Holtzman, David
    Mesulam, M. Marcel
    Potter, William
    Snyder, Peter
    Montine, Tom
    Jimenez, Gustavo
    Donohue, Michael
    Gessert, Devon
    Harless, Kelly
    Salazar, Jennifer
    Cabrera, Yuliana
    Walter, Sarah
    Hergesheimer, Lindsey
    Harvey, Danielle
    Donohue, Michael
    Bernstein, Matthew
    Fox, Nick
    Thompson, Paul
    Schuff, Norbert
    DeCArli, Charles
    Borowski, Bret
    Gunter, Jeff
    Senjem, Matt
    Vemuri, Prashanthi
    Jones, David
    Kantarci, Kejal
    PLOS ONE, 2019, 14 (02):
  • [46] An approach for classification of highly imbalanced data using weighting and undersampling
    Ashish Anand
    Ganesan Pugalenthi
    Gary B. Fogel
    P. N. Suganthan
    Amino Acids, 2010, 39 : 1385 - 1391
  • [47] An approach for classification of highly imbalanced data using weighting and undersampling
    Anand, Ashish
    Pugalenthi, Ganesan
    Fogel, Gary B.
    Suganthan, P. N.
    AMINO ACIDS, 2010, 39 (05) : 1385 - 1391
  • [48] A PREDICTING METHOD OF PUPYLATION SITES FROM IMBALANCED TRAINING DATA
    Tang, Xiaofen
    Liu, Libo
    Liu, Qian
    JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2020, 21 (08) : 1639 - 1653
  • [49] Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data
    Rawal, Ruchit
    Pradhan, Prabhu
    2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020), 2020, : 350 - 359
  • [50] Learning from Imbalanced Data for Predicting the Number of Software Defects
    Yu, Xiao
    Liu, Jin
    Yang, Zijiang
    Jia, Xiangyang
    Ling, Qi
    Ye, Sizhe
    2017 IEEE 28TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE), 2017, : 78 - 89
12345