There are six main categories of breast cancer be existent. In this paper, we have taken the Type 1 carcinoma cancer to support the decision making. For this, a novel machine learning based cost optimization is applied to make an efficient decision from the samples. Moreover, we have applied our methodology on the real datasets to predict cancer with appropriate parameters using Pearson correlation. This work can be used well on lightweight devices like smartphones or tablets to decide more precisely with primary factors.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Cost optimization using normal linear regression method for breast cancer Type I skin


    Beteiligte:


    Erscheinungsdatum :

    2017-04-01


    Format / Umfang :

    256500 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Expectation maximization based logistic regression for breast cancer classification

    Rajaguru, Harikumar / Prabhakar, Sunil Kumar | IEEE | 2017



    Numerical Method for Cost-Weight Optimization of Stringer-Skin Panels

    Richard Curran / Alan Rothwell / Sylvie Castagne | AIAA | 2006


    Linear Regression with Intercept

    Nelson, Eric / Pachter, Meir | AIAA | 2004


    Multiple linear regression analysis

    Edwards, T. R. | NTRS | 1980