Literature DB >> 28401016

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

Rozita Jamili Oskouei1, Nasroallah Moradi Kor2, Saeid Abbasi Maleki3.   

Abstract

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Entities:  

Keywords:  Data mining; cancer diagnosis; cancer prognosis; cancer treatment; medical data; risk factors

Year:  2017        PMID: 28401016      PMCID: PMC5385648     

Source DB:  PubMed          Journal:  Am J Cancer Res        ISSN: 2156-6976            Impact factor:   6.166


  5 in total

1.  Predicting breast cancer survivability: a comparison of three data mining methods.

Authors:  Dursun Delen; Glenn Walker; Amit Kadam
Journal:  Artif Intell Med       Date:  2005-06       Impact factor: 5.326

2.  Analysis of array CGH data for cancer studies using fused quantile regression.

Authors:  Youjuan Li; Ji Zhu
Journal:  Bioinformatics       Date:  2007-07-20       Impact factor: 6.937

3.  Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network.

Authors:  X H Wang; B Zheng; W F Good; J L King; Y H Chang
Journal:  Int J Med Inform       Date:  1999-05       Impact factor: 4.046

4.  Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images.

Authors:  W J Kuo; R F Chang; D R Chen; C C Lee
Journal:  Breast Cancer Res Treat       Date:  2001-03       Impact factor: 4.872

5.  Artificial neural networks improve the accuracy of cancer survival prediction.

Authors:  H B Burke; P H Goodman; D B Rosen; D E Henson; J N Weinstein; F E Harrell; J R Marks; D P Winchester; D G Bostwick
Journal:  Cancer       Date:  1997-02-15       Impact factor: 6.860

  5 in total
  3 in total

1.  Immunological landscape of consensus clusters in colorectal cancer.

Authors:  Pawel Karpinski; Joanna Rossowska; Maria Malgorzata Sasiadek
Journal:  Oncotarget       Date:  2017-10-27

Review 2.  Different Data Mining Approaches Based Medical Text Data.

Authors:  Wenke Xiao; Lijia Jing; Yaxin Xu; Shichao Zheng; Yanxiong Gan; Chuanbiao Wen
Journal:  J Healthc Eng       Date:  2021-12-06       Impact factor: 2.682

3.  Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.

Authors:  Maikel Luis Kolling; Leonardo B Furstenau; Michele Kremer Sott; Bruna Rabaioli; Pedro Henrique Ulmi; Nicola Luigi Bragazzi; Leonel Pablo Carvalho Tedesco
Journal:  Int J Environ Res Public Health       Date:  2021-03-17       Impact factor: 3.390

  3 in total

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