Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

The use of capital budgeting and cost of capital estimation methods in Swedish listed companies

In this paper Swedish listed companies’ use of capital budgeting and cost of capital estimation methods in 2005 and 2008 are examined. The relation between company characteristics and choice of methods is investigated and both within-country longitudinal and cross-country comparisons are made. Larger companies seem to have used capital budgeting methods more frequently than smaller companies. When compared to U.S. and continental European companies, Swedish listed companies employed capital budgeting methods less frequently. In 2005 the most common method for establishing the cost of equity was by asking the investors what return they required. By 2008 CAPM was instead the most utilised method, which could indicate greater sophistication. The use of project risk when evaluating investments also seems to have gained in popularity, while the use of company risk declined. Overall, the use of sophisticated capital budgeting and cost of capital estimation methods seem to be rising and the use of less sophisticated methods declining.

What determines the use of capital budgeting methods? : Evidence from Swedish listed companies

Purpose: This paper aims to extend and contribute to prior research on the association between company characteristics and choice of capital budgeting methods (CBMs). Design/methodology/approach: A multivariate regression analysis on questionnaire data from 2005 and 2008 is used to study which factors determine the choice of CBMs in Swedish listed companies. Findings: Our results supported hypotheses that Swedish listed companies have become more sophisticated over the years (or at least less unsophisticated) which indicates a closing of the theory-practice gap; that companies with greater leverage used payback more often; and that companies with stricter debt targets and less management ownership employed accounting rate of return more frequent. Moreover, larger companies used CBMs more often. Originality/value: The paper contributes to prior research within this field by being the first Swedish study to examine the association between use of CBMs and as many as twelve independent variables, including changes over time, by using multivariate regression analysis. The results are compared to a US and a continental European study.