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Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.

Proteins perform many important functions in the cell and are essential to the health of the cell and the organism. As such, there is much effort to understand the function of proteins. Due to the advances in sequencing technology, there are many sequences of proteins whose function is yet unknown. Therefore, computational systems are being developed and used to help predict protein function. Most computational systems represent proteins using features that are derived from protein sequence or protein structure to predict function. In contrast, there are very few systems that use the biomedical literature as a source of features. Earlier work demonstrated the utility of biomedical literature as a source of text features for predicting protein subcellular location. In this thesis we build on that earlier work, and examine the effectiveness of using text features to predict protein function. Using the molecular function and biological process terms from the Gene Ontology (GO) as our function classes, we trained two classifiers (k-Nearest Neighbour and Support Vector Machines) to predict protein function. The proteins were represented using text features that were extracted from biomedical abstracts based on statistical properties. For evaluation, the performance of our two classifiers was compared to that of two baseline classifiers: one that assigns function based solely on the prior distribution of protein function, and one that assigns function based on sequence similarity. The systems were trained and tested using 5-fold cross-validation over a dataset of more than 36,000 proteins. Overall, we show that text features extracted from biomedical literature can be used to predict protein function for any organism. Our results also show that our text-based classifier typically has comparable performance to the sequence-similarity baseline classifier. Based on our results and what previous work had shown, we believe that text features can be integrated with other types of features to provide more accurate predictions for protein function. / Thesis (Master, Computing) -- Queen's University, 2013-04-24 21:07:13.983

This study investigates factors influencing matriculation decisions for freshman applicants in the College of Agriculture and Life Sciences (CALS) at the University of Arizona. Two different modeling approaches are used on a five-year cross-sectional sample of applicants. Consistent with previous literature, a parametric logistic regression is specified to estimate the probability that a freshman applicant will matriculate in CALS. Additionally, this study also uses non-parametric gradient boosting methods to predict whether an applicant will matriculate. As a byproduct of using two different techniques to model matriculation decisions, an additional academic interest is to see how these two distinct approaches compare in terms of explanation and predictive capabilities. The results show that students who apply early and applicants with high standardized test scores are significantly less likely to matriculate. Moreover, applicants who attend campus tours, honor students, and students from high schools with many applicants are more likely to matriculate.

Telecommunication organizations are confronting in expanding client administration weight as they launch various user-desired services. Conveying poor client encounters puts client connections and incomes at danger. One of the metrics used by telecommunications companies to determine their relationship with customers is “Churn”. After substantial research in the field of churn prediction over many years, Big Data analytics with Data Mining techniques was found to be an efficient way for identifying churn. These techniques are usually applied to predict customer churn by building models, pattern classification and learning from historical data. Although some work has already been undertaken with regards to users’ perspective, it appears to be in its infancy. The aim of this thesis is to validate churn predictive heuristics from the operator perspective and close to user end. Conducting experiments with different sections of people regarding their data usage, designing a model, which is close to the user end and fitting with the data obtained through the survey done. Correlating the examined churn indicators and their validation, validation with the traffic volume variation with the users’ feedback collected by accompanying theses. A Literature review is done to analyze previous works and find out the difficulties faced in analyzing the users’ feeling, also to understand methodologies to get around problems in handling the churn prediction algorithms accuracy. Experiments are conducted with different sections of people across the globe. Their experiences with quality of calls, data and if they are looking to change in future, what would be their reasons of churn be, are analyzed. Their feedback will be validated using existing heuristics. The collected data set is analyzed by statistical analysis and validated for different datasets obtained by operators’ data. Also statistical and Big Data analysis has been done with data provided by an operator’s active and churned customers monthly data volume usage. A possible correlation of the user churn with users’ feedback will be studied by calculating the percentages and further correlate the results with that of the operators’ data and the data produced by the mobile app. The results show that the monthly volumes have not shown much decision power and the need for additional attributes such as higher time resolution, age, gender and others are needed. Whereas the survey done globally has shown similarities with the operator’s customers’ feedback and issues “around the globe” such a data plan issues, pricing, issues with connectivity and speed. Nevertheless, data preprocessing and feature selection has shown to be the key factors. Churn predictive models have given a better classification of 69.7 % when more attributes were provided. Telecom Operators’ data classification have given an accuracy of 51.7 % after preprocessing and for the variables we choose. Finally, a close observation of the end user revealed the possibility to yield a much higher classification precision of 95.2 %.

Objective: To determine the usefulness of the Alvarado score and the Pediatric Appendicitis score (PAS) in the Pediatric Emergency of the National Hospital Daniel A. Carrion. Materials and methods: A prospective observational study was carried out of patients younger than 15 years of age with abdominal pain and suspected acute appendicitis (AA) attending the Pediatric Emergency in a Hospital of Lima, Peru. These patients underwent a survey to assess the parameters of the Alvarado score and PAS. Results: Three hundred and seventeen patients with abdominal pain and suspected of AA were recruited over a study period of 12 months. Of the patients, 232 were considered to have AA clinically and underwent surgery. 85.3% were confirmed by pathology and 14.7% were normal. The mean Alvarado score was 8.27±1.31; the mean Surgical Procedure Assessment (SPA) score was 8.08±1.47. Sensitivity and specificity for both scores are equivalent. The area under the curve for the Alvarado score and SPA were 0.887 and 0.901, respectively. Alvarado score higher than 6 had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 88.9, 75.6, 97.4, 68.1, and 86.4%, respectively. SPA higher than 6 points had sensitivity, specificity, PPV, NPV, and accuracy of 84.3, 80.7, 94.7, 73.1, and 86.7%, respectively. Conclusion: Alvarado score and the PAS are scores with high sensitivity, specificity, PPV, and accuracy for the diagnosis of AA when the score is higher than 6 points. The results found in our study justify their use in emergency services, but they should not be used as the only means of clinically determining the need for surgery.

Call number: LD2668 .T4 1952 B43 / Master of Science

PhD, Faculty of Health Sciences, University of the Witwatersrand, 2008 / Fetal alcohol spectrum disorders (FASD) describe the range of disorders that result from in utero alcohol exposure. FASD is a serious global health problem and is observed at exceedingly high frequencies in certain South African communities. Although in utero alcohol exposure is the primary trigger, there is evidence that genetic- and other susceptibility factors contribute towards FASD development. To date, no genome-wide association or linkage studies have been performed for any of the FASD syndromes. The main objectives of this study were to develop an innovative approach to computationally identify biologically plausible candidate genes for FASD, for a future association study, and to evaluate the appropriateness and validity of this approach. Further, an in silico analysis of known single nucleotide polymorphisms (SNPs) within the top-ranked candidate gene was performed in conjunction with de novo SNP detection, to select a subset of SNPs based on proposed functional impact on gene expression and protein function, for a prospective association study. A computational binary filtering technique was designed that can be employed to prioritize genes in a candidate list, or could be used to rank all genes in the genome in the absence of such a list. 10174 FASD candidate genes were initially selected from the whole genome using a previously described method. Hereafter the candidates were prioritized using a binary filtering technique. The biological enrichment of the ranked genes was assessed by investigating the protein-protein interactions, functional enrichment and common promoter element binding sites of the top-ranked genes. A group of 87 genes was prioritized as candidates highlighting many strong candidates from the TGF-/, MAPK and Hedgehog signalling pathways, which are all integral to fetal development and potential targets for alcohol's teratogenic effect. To assess the effectiveness and accuracy of this computational approach, X-linked mental retardation (XLMR) was used as a test disease, considering that XLMR is a set of heterogeneous disorders of which some of the underlying genetics is known. This implementation resulted in a prioritized gene list with a noted enrichment of known XLMR genes among the top-ranked genes. Furthermore, the top-ranked list contained genes that were biologically relevant to XLMR, and could potentially be as yet unknown candidate genes for XLMR. Indeed, many of the top-ranked genes mapped to XLMR candidate regions, confirming their status as good candidates. Finally, a subset of seven known and novel SNPs was selected within FGFR1 based on putative functional impact. Data from the HapMap project was used to identify tag SNPs for FGFR1 to complement the selection made based on function. The main limitation of the proposed computational approach to candidate gene prediction is that it is primarily based on gene annotation, and that it is therefore biased towards selecting better-annotated genes. However, the results obtained in this study suggest that the described computational method is an effective approach that can identify likely candidates that are biologically relevant to the disease of interest, and therefore appropriate for a candidate-gene association studies. In practice, this technique is an appropriate approach to select a workable set of candidate genes for a complex disease, in a setting where a whole-genome association study is not a viable option.