Browsing by Author "Gittner, Lisaann S. (TTUHSC)"
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Item Does Survival Vary for Breast Cancer Patients in the United States? A Study from Six Randomly Selected States(2017) Khan, Hafiz M.R. (TTUHSC); Gittner, Lisaann S. (TTUHSC); Perisetti, Abhilash (TTUHSC); Saxena, Anshul; Rafiq, Aamrin (TTU); Gabbidon, Kemesha; Mende, Sarah (TTUHSC); Lyuksyutova, Maria (TTUHSC)Background. Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined. Materials and Methods. A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states. Results. Iowa had the highest mean age of diagnosis at 64.14 years (SE=0.324) and Georgia had the lowest at 57.97 years (SE=0.313). New Mexico had the longest mean survival time of 189.09 months (SE=20.414) and Hawaii the shortest at 119.01 (SE=5.394) months, a 70.08-month difference (5.84 years). Analysis of stage of diagnosis showed that the highest survival times for Whites and American Indians/Alaska Natives were for stage I cancers. The highest survival times for Blacks varied. Stage IV cancer consistently showed the lowest survival times. Conclusions. Differences in breast cancer characteristics across states highlight the need to understand differences between the states that result in variances in breast cancer survival.Item Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers(2023) Mondal, Pritom Kumar (TTU); Foysal, Kamrul H. (TTU); Norman, Bryan A. (TTU); Gittner, Lisaann S. (TTUHSC)Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.