In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.

Ex Output:
NeuroSVM: A Graphical User Interface for Identification of Liver Patients


Ex Input:
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-ofthe-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well.

Ex Output:
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis


Ex Input:
This paper studies a new and more general axiomatization than one presented in [6] for preference on likelihood gambles. Likelihood gambles describe actions in a situation where a decision maker knows multiple probabilistic models and a random sample generated from one of those models but does not know prior probability of models. This new axiom system is inspired by Jensen’s axiomatization of probabilistic gambles. Our approach provides a new perspective to the role of data in decision making under ambiguity. 1 Likelihood gambles Likelihood gambles introduced in [5, 6] describe actions in situation of model ambiguity characterized by (1) there are multiple probabilistic models; (2) there is data providing likelihoods for the models and (3) there is no prior probability about the models. Formally, we consider a general problem described by a tuple (X,Y,Θ,A,x). X,Y are variables describing a phenomenon of interest. X is experiment variable whose values can be observed through experiments or data gathering (e.g. lab test results, clinical observations). Y is utility variable whose values determine the utility of actions (e.g. stages of disease, relative size of the tumor). Θ is the set of models that encode the knowledge about the phenomenon. To be precise, Θ is a set of indices and knowledge is encoded in probability functions Prθ(X,Y ) for θ ∈ Θ. A is the set of alternative actions (e.g. surgery, radiation therapy, chemotherapy) that are functions from utility variable Y to the unit interval [0, 1] representing utility. Fi∗I thank Bharat Rao for encouragement and support and UAI-2006 referees for their constructive comments. One should note that the use of utility rather than nally, evidence/data/observation gathered on experiment variable is X = x. A fundamental question to be answered is which among the alternative actions is the best choice given the information. We introduce the concept of likelihood gambles and derive a pricing formula that will allow their comparison. Note that given a model θ ∈ Θ and observation x, distribution on utility variable Y is Prθ(y|x). According to the classical Bayesian decision theory actions a ∈ A are values by their expected utility

Ex Output:

A new axiomatization for likelihood gambles