Advanced Machine Learning Techniques for Cardiovascular Disease Risk Prediction

of mortality, necessitating advanced predictive models to aid early detection and prevention. This study explores the application of machine learning techniques, including Lo- gistic Regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost, to predict CVD risk using a dataset of 69,997 observations encompassing demographic, clinical, and lifestyle factors. Data preprocessing involved one-hot encoding of cat- egorical variables and scaling to ensure compatibility with all models. Model performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Among the models, XGBoost demonstrated the highest accuracy at 74%, leveraging its gradient-boosting framework to effectively handle feature interactions and imbalanced data. Random Forest, with an accuracy of 73%, provided insights into feature importance, highlighting systolic blood pressure and age as critical predictors. In contrast, KNN exhibited lower performance at 66%, attributed to its sensitivity to scaling and high-dimensional data. These findings underscore the potential of ensemble methods like XGBoost and Random Forest in clinical decision-making and public health strategies for mitigating CVD risks

AI-Integrated Cross-Disciplinary Liberal Arts Colloquium

What began organically as an AI-focused collaboration between faculty in different disciplines along with researchers in the college’s Center for Instructional Innovation, has evolved into college-wide pedagogical workshops and the development of a studio model that affects courses, programs, and paradigms for faculty scholarship. Lessons from these collaborations inform understandings of how key features of liberal arts education can be supported through intentional AI-integration within a Cross-Disciplinary Liberal Arts Colloquium. More specifically, this colloquium offers recommendations on adapting a collaborative, interdisciplinary, and reflective studio model to in-person, online, and hybrid educational environments facilitated through a Technological Pedagogical Content Knowledge (TPACK) frame work (Mishra, P., & Koehler, M. J., 2006)

Florida Native American Tribes Unit Plan

Designed by pre-service teachers, this fifteen-day Social Studies unit covers 4th grade Florida Social Studies Sunshine State Standards and Florida B.E.S.T. Standards while incorporating Universal Design for Learning strategies, English Language strategies, and opportunities for students to engage with educational technology in various capacities. This unit is complete with a daily Instructional Guide for teachers, turnkey student-facing materials, lists of or links to any necessary resources not created by the authors, and a post-assessment. During this unit, students will learn about Native American tribes in Florida. To enhance student knowledge, teachers will either facilitate a virtual tour of the “First Peoples” Exhibit at the Orange County Regional History Center that was created by the pre-service teacher authors or organize a First Floridians field trip to the Orange County Regional History for their students. Using a variety of reading materials and resources, students will explore and compare various cultural aspects of the Timucua, Calusa, and Seminole tribes. As a culminating activity, students will apply their new knowledge of Native Floridians by creating roleplay presentations in collaborative groups

Florida Frontiers Radio Program #574

SEGMENTS | La Florida Digital Archive | Hillsborough River Environmental History | The \u27Unknown\u27 Indian Graves in St. Augustin

Comparison of Two Strategies of Screening Experiments: Single-shot Experiment vs. Two-stage Screening Experiment

Experiments involving many factors are often complex, time-consuming, and expensive. Screening out the least important factors helps the experimenter(s) allocate the limited resources efciently to the most important factors. Supersaturated and orthogonal array designs are among the designs used to conduct screening experiments. Supersaturated designs (SSDs) are those where the number of runs (observations) is less than the number of factors, while orthogonal array (OA) designs are those where at least the columns are orthogonal to each other. In this study, we conduct a simulation study to compare two strategies of screening experiments. Strategy one is a single shot experiment using an orthogonal array. Strategy two is a two-stage screening experiment that involves supersaturated design in the frst stage and a follow-up using orthogonal array design in the second stage. The study investigates two models: (1) a model with a subset of main efects being active and (2) a model with a subset of main efects and some two-factor interaction effects active. The two strategies are analyzed via the Dantzig selector method. The power to detect active efects, type I error rate, and false discovery rate are computed and compared. Generally, strategy one performs better than strategy two. When efect sparsity is high, the two strategies are comparable

A Report on Health Care Access by the United States Citizens.

Access to health care is a critical factor in ensuring public health. This study analyzes data from the National Health Interview Survey (NHIS) for the years 2015–2018 to examine the relationship between health care coverage, affordability, and costs among U.S. families. Re-sults indicate that families with at least one member covered by health insurance were more likely to afford medical care and incur lower health care costs. Despite a high proportion of families with health care coverage during this period, the number of insured family members declined over the years. These findings underscore the importance of health care coverage in reducing financial barriers to medical access and highlight areas for further exploration in health policy

Handwritten Digit Recognition using Naive Bayes and K-Nearest Neighbor Models

This paper explores the performance of two fundamental classifcation algorithms. It uses Naive Bayes and K-Nearest Neighbors (KNN), framing it within the context of digit recognition of the MNIST dataset. The MNIST dataset has 70,00 grayscale images of handwritten digits, offering a standard for assessing classifcation models. This paper focuses on key performance metrics such as precision, accuracy, recall, and F1score to examine the effciency of each model. The results reveal that Naive Bayes has moderate accuracy and misclassifcations because of its notion of feature independence. The paper concludes that the KNN model performs better with the optimal k-value of 3, producing the highest accuracy and reducing misclassifcation rates. The comparative analysis helps identify each model’s strengths and limitations and emphasizes the need to explore advanced models in improving and understanding linear classifcation

Investigation Into Whether Chlamydia Can Successfully Invade Ovarian Cancer Cells

Chlamydia trachomatis (C. trachomatis) is being investigated as a potential vector to directly deliver anti-cancer peptides to ovarian cancer cells as part of the “Bugs as Drugs” initiative, offering a possible future treatment option for women diagnosed with ovarian cancer. Successful delivery of peptides from chlamydia to host cells first requires elementary bodies (EBs), the metabolically inactive form of chlamydia, to gain entry to host cells. To test the viability of this proposition, this project aimed foremost to demonstrate whether C. trachomatis is even capable of infecting ovarian cancer cell strains in vitro. Serovar L2 C. trachomatis was chosen and incubated along with three separate ovarian cancer cell lines: SK-OV-3, PA-1, and SW 626. A HeLa control sample was also grown alongside and incubated with chlamydia. Further experiments were done with the addition to the media of chloramphenicol (CHL), a known antibiotic and inhibitor of bacterial translation. CHL was added to ascertain whether infection could also preclude the development of reticulate bodies (RBs) (the metabolically active form of chlamydia) which could potentially harm healthy cells in the ovaries. Qualitatively, successful invasion of all three cell lines by chlamydia was observed. Encouragingly, the addition of CHL produced the same results but also demonstrated that host cell infections did not result in the development of RBs. Furthermore, western blots done on collected cell lysates showed consistency with other previously published research on chlamydial host cell infections, adding greater validity to the results. These findings encourage the continuation of future experiments that will test whether genetic modifications, such as the addition of nucleotides encoding the CT20 anti-cancer peptide, or administration of chlamydia along with different reagents, produces more promising results for a potential new ovarian cancer treatment option