Browsing by Subject "Decision support"

Now showing 1 - 4 of 4

Open Access
A comprehensive methodology for determining the most informative mammographic features
(2013) Wu, Y.; Alagoz O.; Ayvaci, M.U.S.; Munoz Del Rio, A.; Vanness, D.J.; Woods, R.; Burnside, E.S.
This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon's entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis. © 2013 Society for Imaging Informatics in Medicine.
Open Access
A decision consultancy case on strategy formulation of a livestock project
(1994) Akar, Mustafa
The purpose of this thesis is to assist in a project that combines decision support mechanisms and their probable results to assist top management in decision making. Government regulation obliges the subject firm to enter daily cattle artificial insemination business. This study covers an analysis for the industry structure, financial estimation and present value calculations, suggested value chain, marketing strategy, and sei-vice guarantee - quality with a motivation program.
Open Access
A decision support tool for fund management
(1994) Polatoğlu, Melike Aslı
The purpose of this thesis is (1) to provide an overview for asset liability management approach to the bank management, and (2) to develop a tool for the senior management of a bank, which will be useful for the decision making process. The software developed will enable managers to see the risks involved, therefore make it easier to hedge them. The software will provide the user with profit-loss projections for given scenarios of the economy (FC devaluation, interest rates,etc.) for a certain structure of the balance sheet. Users will also be able to observe the changes in the profit/loss with changes in the items of the balance sheet for given economic scenarios.
Open Access
Ranking instances by maximizing the area under ROC curve
(Institute of Electrical and Electronics Engineers, 2013) Guvenir, H. A.; Kurtcephe, M.
In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a single categorical feature, we show the necessary and sufficient condition that any ranking function must satisfy to achieve the maximum AUC. We also sketch a method to discretize a continuous feature in a way to reach the maximum AUC as well. RIMARC uses a heuristic to extend this maximization to all features of a data set. The ranking function learned by the RIMARC algorithm is in a human-readable form; therefore, it provides valuable information to domain experts for decision making. Performance of RIMARC is evaluated on many real-life data sets by using different state-of-the-art algorithms. Evaluations of the AUC metric show that RIMARC achieves significantly better performance compared to other similar methods. © 1989-2012 IEEE.