Literature DB >> 21837454

Prediction of breast cancer using artificial neural networks.

Ismail Saritas1.   

Abstract

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.

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Year:  2011        PMID: 21837454     DOI: 10.1007/s10916-011-9768-0

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  11 in total

1.  Improving ultrasonographic diagnosis of prostate cancer with neural networks.

Authors:  A L Ronco; R Fernández
Journal:  Ultrasound Med Biol       Date:  1999-06       Impact factor: 2.998

2.  Perceptron error surface analysis: a case study in breast cancer diagnosis.

Authors:  Mia K Markey; Joseph Y Lo; Rene Vargas-Voracek; Georgia D Tourassi; Carey E Floyd
Journal:  Comput Biol Med       Date:  2002-03       Impact factor: 4.589

Review 3.  Mammographic breast density: effect on imaging and breast cancer risk.

Authors:  Renee W Pinsky; Mark A Helvie
Journal:  J Natl Compr Canc Netw       Date:  2010-10       Impact factor: 11.908

4.  Computer aid for decision to biopsy breast masses on mammography: validation on new cases.

Authors:  Anna O Bilska-Wolak; Carey E Floyd; Joseph Y Lo; Jay A Baker
Journal:  Acad Radiol       Date:  2005-06       Impact factor: 3.173

5.  Bayesian networks of BI-RADStrade mark descriptors for breast lesion classification.

Authors:  E A Fischer; J Y Lo; M K Markey
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2004

6.  Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions.

Authors:  C E Floyd; J Y Lo; G D Tourassi
Journal:  AJR Am J Roentgenol       Date:  2000-11       Impact factor: 3.959

7.  Clinically occult ductal carcinoma in situ detected with mammography: analysis of 100 cases with radiologic-pathologic correlation.

Authors:  P C Stomper; J L Connolly; J E Meyer; J R Harris
Journal:  Radiology       Date:  1989-07       Impact factor: 11.105

8.  Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.

Authors:  J A Baker; P J Kornguth; J Y Lo; M E Williford; C E Floyd
Journal:  Radiology       Date:  1995-09       Impact factor: 11.105

9.  Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon.

Authors:  Anna O Bilska-Wolak; Carey E Floyd
Journal:  Med Phys       Date:  2002-09       Impact factor: 4.071

10.  Classification models for early detection of prostate cancer.

Authors:  Joerg D Wichard; Henning Cammann; Carsten Stephan; Thomas Tolxdorff
Journal:  J Biomed Biotechnol       Date:  2008
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  14 in total

1.  Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation.

Authors:  Sevcan Aytac Korkmaz; Mehmet Fatih Korkmaz; Mustafa Poyraz
Journal:  Med Biol Eng Comput       Date:  2015-09-07       Impact factor: 2.602

2.  Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree.

Authors:  Cheng-Min Chao; Ya-Wen Yu; Bor-Wen Cheng; Yao-Lung Kuo
Journal:  J Med Syst       Date:  2014-08-14       Impact factor: 4.460

3.  Heart motion uncertainty compensation prediction method for robot assisted beating heart surgery - Master-slave Kalman Filters approach.

Authors:  Fan Liang; Yang Yu; Shigang Cui; Li Zhao; Xingli Wu
Journal:  J Med Syst       Date:  2014-05-01       Impact factor: 4.460

4.  Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

Authors:  Victoria L Mango; Mary Sun; Ralph T Wynn; Richard Ha
Journal:  AJR Am J Roentgenol       Date:  2020-04-22       Impact factor: 3.959

5.  A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.

Authors:  A Suresh; R Udendhran; M Balamurgan; R Varatharajan
Journal:  J Med Syst       Date:  2019-05-03       Impact factor: 4.460

6.  A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

Authors:  Binghuang Cai; Xia Jiang
Journal:  J Biomed Inform       Date:  2013-12-18       Impact factor: 6.317

7.  A novel and reliable computational intelligence system for breast cancer detection.

Authors:  Amin Zadeh Shirazi; Seyyed Javad Seyyed Mahdavi Chabok; Zahra Mohammadi
Journal:  Med Biol Eng Comput       Date:  2017-09-11       Impact factor: 2.602

8.  γ -H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer.

Authors:  E Chatzimichail; D Matthaios; D Bouros; P Karakitsos; K Romanidis; S Kakolyris; G Papashinopoulos; A Rigas
Journal:  Int J Genomics       Date:  2014-01-08       Impact factor: 2.326

9.  Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation.

Authors:  Abdeldjalil Khelassi
Journal:  Electron Physician       Date:  2014-11-27

10.  BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins.

Authors:  MuthuKrishnan Selvaraj; Munish Puri; Kanak L Dikshit; Christophe Lefevre
Journal:  Adv Bioinformatics       Date:  2016-02-29
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