Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions

Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions

Regularized Functional Regression Models with Applications to Brain Imaging.

Positron emission tomography (PET) is an imaging technique that provides useful information about brain metabolism to help clinicians in the early diagnosis of Alzheimer's disease (AD). In order to identify the brain areas that show significant signals, many statistical methods have been developed for the analysis of brain imaging data. However, most of them neglect accounting for spatial information in imaging data. One way to address this problem is to treat each image as a realization of a functional predictor. This dissertation includes three research projects concerning regularized functional regression models via Haar wavelets for the analysis of brain imaging data, particularly PET images. The first project develops a lasso penalized 3D functional linear regression model by viewing PET image as a 3D functional predictor and cognitive impairment as the response variable, aiming to identify the most predictive voxels with the underlying assumption that only a few brain areas are truly predictive. The PET images are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The second project concerns a lasso penalized 3D functional logistic regression model for classification of PET images from ADNI database. ADNI participants were classified into three groups during their initial visits: AD, Mild Cognitive Impairment (MCI) and Normal Control (NC). The model is applied to all the pairwise classifications using baseline PET images. The third project develops a regularized 3D multiple functional logistic regression model that can account for the group structure among voxels. Cerebral cortex can be partitioned into multiple regions. Treating each region as a group, within-group and groupwise regularization is imposed into the estimation to identify the most predictive voxels. This model is applied to the prediction of MCI-to-AD conversion using ADNI MCI subjects’ baseline PET images. All proposed models are evaluated through extensive simulation studies which are based on simulated data and slices extracted from ADNI PET images. Comparisons with existing methods for the prediction performance are also conducted using ADNI data. The results suggest that the proposed models are able to not only identify the predictive voxels, but also achieve higher prediction accuracy than existing methods in general.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99975/1/xuejwang_1.pd

Optical spectroscopy and imaging systems for gynecological cancers: from Ultraviolet-C (UVC) to the Mid-infrared

Optical spectroscopy and imaging has proving to be of diagnostic relevance in many organ sites. We use fluorescence and FTIR spectroscopy to study gynecological organ sites and develop classification algorithms for cancer diagnosis. Ovarian cancer is the deadliest gynecological cancer. The American Cancer Society reports that for the year 20 I 0, there would be 21,880 new cases of ovarian cancer and 13,850 fatalities. This is partly due to the fact that current diagnostic and screening methods for the disease are not very accurate. In this study, we analyze the fluorescence spectra of excised normal and cancerous ovarian tissues at multiple excitation wavelengths. The data includes spectra obtained at the UVC wavelength 270nm and UVB wavelength 300nm. Excitation in the UVC has been especially understudied in spectroscopy for tissue diagnosis. We introduce the application of a novel SVM algorithm for the classification of fluorescence data. This SVM is trained subject to the Neyman Pearson (NP) criterion which allows for a decision rule that maximizes the detection specificity whilst constraining the sensitivity to a high value. This technique allows us to develop a binary classification algorithm that is not biased towards the larger group and this in tum leads to robust classifiers that are more suitable for clinical applications. We obtained sensitivities and specificities greater than 90% for ovarian cancer diagnosis using this algorithm. Also, FTIR is used to analyze cervical tissues. Absorption of light in the mid-IR region by biomolecules show up as peaks in the FTIR spectra, and there is differential absorption in tissue depending on the histopathology. The spectroscopic analysis informed our choosing of a wavelength for the illumination source ofa mid-IR microscope. We then present the design of an imaging system that employs the use ofa mid-IR quantum cascade laser(QCL) which can potentially have clinical use in the future. Finally a reflectance based fiber endoscope imaging system is presented. Cellular imaging is demonstrated with this system that has the potential for use in optical biopsy