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The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease. © 2024, Zarka Private University. All rights reserved.
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The search for selective anticholinergic agents stems from varying cholinesterase levels as Alzheimer’s Disease progresses from the mid to late stage. In this computational study, we probed the selectivity of FDA-approved and metabolite compounds against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with molecular-docking-based virtual screening. The results were evaluated using locally developed codes for the statistical methods. The docking-predicted selectivity for AChE and BChE was predominantly the consequence of differences in the volume of the active site and the narrower entrance to the bottom of the active site gorge of AChE. © 2024 by the authors.
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This final commentary, in comic format, frames this special issue using Graphic Medicine methodologies to explore broader themes and meanings related to the scientific study of gender and health. Comics can be seen as a way to introduce complex human narratives and as an exploratory tool to ask broader social-contextual and ethical questions about health and medicine. This piece is also constructed through the lens of queer scholarship, which, together with the comics format, provides opportunities to build more embodied, complicated narratives about gender, sexuality and health. Most importantly, comics are used as a modality to tell compelling narratives about how individuals, rather than populations, may be impacted by biomedical conceptualizations of gender and health. The commentary includes a series of graphic narratives containing hypothetical stories and cases: stories of how individuals may be harmed within healthcare systems by rigid framings of gender, sex and sexuality, and stories about how gender socialization may impact health in subtle ways. These narratives furthermore examine the inextricable link between gender and power, illustrating how overt and covert manifestations of power may shape a person's health over the life course. Finally, the piece explores how expansive views of gender may contribute to positive health care experiences. The intention of this piece is to nudge scientific researchers and clinicians alike to approach the topic of gender, sexuality and health with nuance and curiosity. © 2023 The Author(s)
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