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  • br Fig Total possible enumeration for a

    2019-10-09


    Fig. 5. Total possible enumeration for a discomfort value 10.
    Fig. 6. Number of FA with sensitivity (in %) for various enumeration of discomfort.
    [55], Probably Approximate Correct (PAC) learning of a binary classi-fier from noisy labeled examples acquired from multiple annotators [56], or a single unbiased estimator [57] may direct to a novel path for achieving the above objective. Also, chance-constrained programs [58], may be studied to design a conservative classifier which makes better tradeoffs between noise tolerance and making the FN zero.
    Summary table
    Known artifacts before this study
    • EMR helps in predicting cardiac problems, breast cancer, lung tumor and many other diseases but yet to contribute in predicting eso-phageal cancer, which is rising worldwide.
    • Machine learning plays as a critical instrument in early detection of a disease.
    Knowledge addition by this research
    • Demographic, lifestyle and basic clinical data (without doctor's su-pervision) can predict Esophageal cancer with a very high accuracy.
    • A feature transformation to a higher space can have a higher ac-curacy than orthodox machine learning methods and yield a high sensitivity to detect all true patients.
    • Customized tests using a subset of features(tests) still can predict the esophageal cancer without compromising the probability of non inclusion of a true patient. • The novel idea of selecting a subset of standard tests to predict a disease can help many stakeholders – patient, doctor, insurance provider and others by reducing cost, improving quality or opti-mizing service parameters.
    References
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    [43] Scholkopf B, Sung K-K, Burges CJC, Girosi F, Niyogi P, Poggio T, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers.  Artificial Intelligence In Medicine 95 (2019) 16–26
    [47] Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J. LIBLINEAR: a periosteum library for large linear classification. J Mach Learn Res 2008;9:1871–4 Software available from: http://www.csie.ntu.edu.tw/cjlin/liblinear. [48] Schölkopf B, Smola AJ. Learning with kernels: support vector machines, regular-ization, optimization, and beyond. MIT Press; 2002. [49] Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The weka data mining software: an update. SIGKDD Explorat 2009;11:1871–4 software available at: http://www.csie.ntu.edu.tw/cjlin/liblinear. [50] Chang C-C, Lin C-J. Libsvm: a library for support vector machines. ACM Trans Intell
    26 International Journal of Radiation Oncology biology physics
    www.redjournal.org
    Clinical Investigation