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Selection of an appropriate classifier for computer-aided diagnosis (CAD) applications has typically been an ad hoc process. It is difficult to know a priori which classifier will yield high accuracies for a specific application, especially when well-annotated data for classifier training is scarce. In this study, we utilize an inverse power-law model of statistical learning to predict classifier...
Accurate and less invasive personalized predictive medicine relieves many breast cancer patients from agonizingly complex surgical treatments, their colossal costs and primarily letting the patient to forgo the morbidity of a treatment that proffers no benefit. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. Support...
Hemostatic parameters or parameters related to blood clotting are useful for diagnosis and prognosis. This is because abnormalities in hemostatic parameters are observed in various diseases. However, these parameters vary among localities and individuals. Thus, computational intelligent tool is required to aid the diagnosis and prognosis using hemostasis. Genetic complementary learning (GCL) is a...
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