Development and realization of the artificial neural network for diagnostics of stroke type

被引:0
|
作者
Rebrova, OY [1 ]
Ishanov, OA [1 ]
机构
[1] Inst Neurol, Moscow, Russia
来源
ARTIFICIAL NEURAL NETWORKS: BIOLOGICAL INSPIRATIONS - ICANN 2005, PT 1, PROCEEDINGS | 2005年 / 3696卷
关键词
artificial neural network; medical diagnostics; web-based application; perceptron; stroke;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Methods of artificial neural networks are applied to the development of the decision support system for differential diagnostics of three types of stroke. Diagnostic sensitivity and positive predictive value were used as the basic criteria for estimation of efficiency of the developed algorithm. Their values appeared to be 97% and 99% respectively, and these results significantly exceed both the existing level of physicians' diagnostics, and the efficiencies of statistical algorithms developed earlier. C-code was generated, and web-based application was realized. Research of algorithm's efficiency continues.
引用
收藏
页码:659 / 663
页数:5
相关论文
共 50 条
  • [21] Development of a neural network technique for KSTAR Thomson scattering diagnostics
    Lee, Seung Hun
    Lee, J. H.
    Yamada, I.
    Park, Jae Sun
    REVIEW OF SCIENTIFIC INSTRUMENTS, 2016, 87 (11):
  • [22] Artificial Neural Network for the Urinary Lithiasis Type Identification
    Mekki, Yasmina Nozha
    Farah, Nadir
    Boutefnouchet, Abdelatif
    Chettibi, KheirEddine
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II, 2016, 9887 : 542 - 543
  • [23] AR–ARCH Type Artificial Neural Network for Forecasting
    Burcin Seyda Corba
    Erol Egrioglu
    Ali Zafer Dalar
    Neural Processing Letters, 2020, 51 : 819 - 836
  • [24] Recognizing imperfections with an artificial neural network of a special type
    Barkhatov, VA
    RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING, 2006, 42 (02) : 92 - 100
  • [25] Recognizing imperfections with an artificial neural network of a special type
    V. A. Barkhatov
    Russian Journal of Nondestructive Testing, 2006, 42 : 92 - 100
  • [26] Artificial intelligence for the diagnostics of gas turbines - Part I: Neural network approach
    Bettocchi, R.
    Pinelli, M.
    Spina, P. R.
    Venturini, M.
    JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2007, 129 (03): : 711 - 719
  • [27] FAULTS DIAGNOSTICS OF CEMENT DRAFT FAN USING ARTIFICIAL NEURAL NETWORK (ANN)
    Menasri, Noureddine
    Aimeur, Noureddine
    STRUCTURAL INTEGRITY AND LIFE-INTEGRITET I VEK KONSTRUKCIJA, 2023, 23 (01): : 23 - 29
  • [28] An artificial neural network for five different PSA assays in prostate cancer diagnostics
    Stephan, C.
    Cammann, H.
    Miller, K.
    Deger, S.
    Lein, M.
    Jung, K.
    EUROPEAN UROLOGY SUPPLEMENTS, 2008, 7 (03) : 140 - 140
  • [29] VLSI realization of neural network
    Tan, Xilin
    Hu, Jincai
    Lang, Wayne
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 1993, 21 (04): : 98 - 100
  • [30] NUCLEAR-POWER-PLANT STATUS DIAGNOSTICS USING AN ARTIFICIAL NEURAL NETWORK
    BARTLETT, EB
    UHRIG, RE
    NUCLEAR TECHNOLOGY, 1992, 97 (03) : 272 - 281