Physical Sciences and Mathematics Commons^™

Analysis Of Artificial Neural Networks In The Diagnosing Of Breast Cancer Using Fine Needle Aspirates, Janette Vazquez

Theses and Dissertations

This thesis examines how Artificial Neural Networks can be used to classify a set of samples from a fine needle aspirate dataset. The dataset is composed of various different attributes, each of which are used to come to the conclusion as to whether a sample is benign or malignant. To automate the process of analyzing the various attributes and coming to a correct prediction, a neural network was implemented. First, a Feedforward Neural Network was trained with the dataset using a Backpropagation training method and an activation sigmoid function with one hidden layer in the architecture of the network. After …

An Investigation Of Factors That Influence Registered Nurses’ Intentions To Use E-Learning Systems In Completing Higher Degrees In Nursing, Pauline G. Little

CCE Theses and Dissertations

There is an increasing demand for more baccalaureate- and graduate-prepared registered nurses in the United States, to face the healthcare challenges of the 21st century. As a strategy to meet this need, educational institutions are expanding electronic learning in nursing education; however, technology acceptance in education continues to be a concern for educational institutions. In this context, the goal of the study was to investigate factors that potentially influence registered nurses’ intentions to adopt e-learning systems. A theoretical model was used to determine whether perceived value, attitude toward e-learning systems, and resistance to change influence registered nurses’ intentions to use …

Consumer Adoption Of Health Information Systems, Sankara Subramanian Srinivasan

Graduate Theses and Dissertations

At nearly 18 percent of the country's GDP, the U.S. healthcare industry continues to wrestle with growing cost and a quality of care that does not match the increased spending. The dominant focus to date has been on promoting Health IT (HIT) system implementation and digitizing health records at the provider's end, with scant attention to the role of the patient in the healthcare process. The source of inefficiency in the healthcare system is not only on account of shortcomings at the provider's end but also due to non-compliance (such as failing to adhere to medication advice and follow-up visits) …

Understanding User Resistance To Information Technology: Toward A Comprehensive Model In Health Information Technology, Madison N. Ngafeeson

Theses and Dissertations - UTB/UTPA

The successful implementation of health information systems is expected to increase legibility, reduce medical errors, boost the quality of healthcare and shrink costs. Yet, evidence points to the fact that healthcare professionals resist the full use of these systems. Physicians and nurses have been reported to resist the system. Even though resistance to technology has always been identified as key issue in the successful implementation of information technology, the subject remains largely under-theorized and deficient of empirical testing. Only two proposed model have been tested so far. Hence, though user resistance is clearly identified and defined in literature, not very …

Associative Pattern Mining For Supervised Learning, Harpreet Singh

Doctoral Dissertations

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known …

Antes: A Web-Based Acanthosis Nigricans And Other Obesity Related Information System, Chunyue Wang

Theses and Dissertations - UTB/UTPA

Acanthosis nigricans is a cutaneous marker associated with systemic disorders and may serve as an indicator for risk of Type 2 diabetes. Acanthosis nigricans screening can help identify children who have high insulin levels and who may be at-risk for developing Type 2 diabetes. The ANTES system is a computerization attempt for acanthosis nigricans control of the student population from elementary schools and secondary schools in Texas. A general description of the system and the medical and history background of the ANTES program is given. The technology applied to the system is demonstrated. An overview of the system operation status …

Data Analysis In The Antes System, Yavuz Tor

Theses and Dissertations - UTB/UTPA

Acanthosis nigricans is a skin condition that can be used as an indicator for the risk of developing type 2 diabetes in the future. Border Health Office, in University of Texas - Pan American, organizes screenings in schools for acanthosis nigricans. Screening results are, then, collected and evaluated in the Border Health Office. The ANTES System is a computer system that stores and manages the data collected in those screenings.

This study is on the analysis of those collected data to track the progress of data entry, to evaluate the progress on obesity and related problems, and to discover the …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …