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  • br Fig Total possible enumeration for a


    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.
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    Clinical Investigation