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Data Mining Techniques In Computer Aided Cancer Diagnosis
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Data Mining Techniques In Computer Aided Cancer Diagnosis



Data Mining Techniques In Computer Aided Cancer Diagnosis

Data Mining Techniques In Computer Aided Cancer Diagnosis



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Data Mining Techniques In Computer Aided Cancer Diagnosis Data Mining Techniques In Computer Aided Cancer Diagnosis Presentation Transcript

  • Data mining techniques in computer aided cancer diagnosis
  • Data mining in cancer diagnosis
    Bio medical data mining happens to be a long standing problem in scientific research where scientists are looking for innovative ideas methods to mine bio-medical data.
    Data mining in CAD(computer aided diagnosis) helps doctors to make optimal decisions quickly and accurately.
  • Data mining techniques allow the doctors to quickly categorize the difference between malignant and benign tumors.
    Key factor analysis is done to find the difference between benign and tumor cells.
    Decision trees may be used for clustering, classification, prediction or estimation.
    One of the useful applications in medical sciences is in management of leukemia as it accounts for about 33% of pediatric malignancies.
  • ALL
    Child acute lymphatic leukemia( also called acute lymphatic leukemia or ALL) is a cancer of blood and bone marrow.
    Its most common in children and gets worse if not treated in early stages.
  • Key data mining techniques used in ALL diagnosis are:
    Neural networks
    Decision trees
    Cluster detection
    Genetic algorithms
  • Benefits of using data mining techniques in CAD
    Data mining techniques help physicians in a variety of ways:
    • To interpret complex diagnosis
    • Combine information from multiple sources
    • Provide support for differential diagnosis
    • Providing patient-specific prognosis.
  • Data mining techniques in detecting gastric cancer
    A diagnosis model using data mining with knowledge discovery techniques and single nucleotide polymorphism(SNP) information can be used to detect gastric cancer in humans.
    Case-based reasoning can be applied to cancer detection problem.
  • Data warehousing, data mining, and decision support systems to reduce national cancer burden
    An information system is presented to deliver the technology and knowledge that users need readily to:
    • Organize relevant claims data
    • Detect cancer patterns in general and special populations
    • Formulate models that explains the patterns
    • Evaluate the efficacy of specified treatments and interventions with the formulations.
  • Analysis of breast cancer using data mining
    Data mining and statistical analysis is the search for valuable information in large volumes of data.
    New applications of data mining and neural networks are helping doctors to detect cancers sooner.
    AL tools and neural networks of pattern recognition can help indicate the presence of breast cancer.
  • New image mining techniques for breast cancer detection
    The method uses an overlapped technique to precisely detect the presence of breast cancer.
    Statistical features are extracted from mammograms and used for decision tree induction in order to learn the knowledge for computer assisted image analysis.
  • The application of data mining will help to get some additional knowledge about specific features of different classes and the in which they are expressed in the image (can help to find some inherent non-evident links between classes and their imaging in the picture)
    It can help to get some non-trivial conclusions and predictions can be made on the base of image analysis.
    The basis for this study is sufficiently a large database with images and expert descriptions.
  • National cancer institute-3D mind tools
    National cancer institute came up with 3D mind tools which help scientists to detect compounds that are related, compare their activities, and test hypothesis about the anti-cancer mechanisms.
    Other interesting results of 3D mind tools:
    • A compound card builder that allows users to pick and choose molecules from among 30,000 different compounds.
    • A data viewer that provides a series of reports.
    • A map viewer that allows users to project compounds onto color maps for rapid visual analysis.
    • Links to external publications databases for related articles of interests.