Prediction of Breast Cancer Using Artificial Neural Networks

被引:38
|
作者
Saritas, Ismail [1 ]
机构
[1] Selcuk Univ, Tech Educ Fac, Dept Elect & Comp Educ, Konya, Turkey
关键词
Artificial neural network; BI-RADS; Breast cancer; Breast cancer prediction; DIAGNOSIS; DECISION;
D O I
10.1007/s10916-011-9768-0
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.
引用
收藏
页码:2901 / 2907
页数:7
相关论文
共 50 条
  • [21] Prediction of fingers posture using artificial neural networks
    Rezzoug, Nasser
    Gorce, Philippe
    [J]. JOURNAL OF BIOMECHANICS, 2008, 41 (12) : 2743 - 2749
  • [22] STOCK MARKET PREDICTION USING ARTIFICIAL NEURAL NETWORKS
    Bharne, Pankaj K.
    Prabhune, Sameer S.
    [J]. PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS), 2019, : 64 - 68
  • [23] Medical Image Prediction Using Artificial Neural Networks
    Xhako, Dafina
    Hyka, Niko
    [J]. TURKISH PHYSICAL SOCIETY 35TH INTERNATIONAL PHYSICS CONGRESS (TPS35), 2019, 2178
  • [24] Prediction of lake eutrophication using artificial neural networks
    Huo, Shouliang
    He, Zhuoshi
    Su, Jing
    Xi, Beidou
    Zhang, Lieyu
    Zan, Fengyu
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION, 2015, 56 (1-4) : 63 - 78
  • [25] Prediction of crossroad passing using artificial neural networks
    Civilis, Alminas
    [J]. 2006 SEVENTH INTERNATIONAL BALTIC CONFERENCE ON DATABASES AND INFORMATION SYSTEMS - PROCEEDINGS, 2006, : 229 - 234
  • [26] NFL Prediction using Committees of Artificial Neural Networks
    David, John A.
    Pasteur, R. Drew
    Ahmad, M. Saif
    Janning, Michael C.
    [J]. JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS, 2011, 7 (02)
  • [27] Prediction of Sediment Concentration Using Artificial Neural Networks
    Dogan, Emrah
    [J]. TEKNIK DERGI, 2009, 20 (01): : 4567 - 4582
  • [28] Time series prediction using artificial neural networks
    Pérez-Chavarríia, MA
    Hidalgo-Silva, HH
    Ocampo-Torres, FJ
    [J]. CIENCIAS MARINAS, 2002, 28 (01) : 67 - 77
  • [29] Prediction of hydrocyclone performance using artificial neural networks
    Karimi, M.
    Dehghani, A.
    Nezamalhosseini, A.
    Talebi, Sh
    [J]. JOURNAL OF THE SOUTH AFRICAN INSTITUTE OF MINING AND METALLURGY, 2010, 110 (05): : 207 - 212
  • [30] Stability Prediction of ΔΣ Modulators using Artificial Neural Networks
    Kaesser, Paul
    Kaltenstadler, Sebastian
    Conrad, Joschua
    Wagner, Johannes
    Ismail, Omar
    Ortmanns, Maurits
    [J]. 2024 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS 2024, 2024,