AUS Repository

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    A Bayesian Approach to Feature Selection in Classification Problems
    (2024-07) Emarly, Maher; Alzaatreh, Ayman
    The exponential growth of data, as well as the widespread use of machine learning in daily life, demonstrate the importance of feature selection. Feature selection, defined as the process of identifying and selecting a subset of relevant features from a larger set of available features, is a crucial step in machine learning. The performance and efficiency of machine learning models are improved by focusing on the most informative features and eliminating unnecessary or redundant ones. Furthermore, model interpretability is enhanced, resulting in clearer insights and an actionable understanding of the results. The resulting models are more robust, less prone to noise, and can be efficiently trained and deployed, ultimately contributing to more effective and efficient data-driven decision-making processes. We propose a Bayesian approach using the relative belief ratio (RBR) as a filter method in this paper. The proposed method showed an excellent performance in binary and multiclass classification problems. In addition, the proposed method generates a strength value that can be used as an importance score for each feature. The numerical value of the strength of the RBR is used to rank the features. This method aims to discern the relative importance of features concerning a target variable and test for their significance. The proposed method’s performance is evaluated using both synthetic and real-world datasets, and it is compared to various popular filter methods.
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    Outlier Detection Using the Relative Range Distribution
    (2024-07) Dallah, Dania; Sulieman, Hana; Alzaatreh, Ayman
    Outlier detection plays a crucial role in data analysis. Outlier detection is a challenging task due to the subjective nature of defining what constitutes an outlier. By identifying and appropriately handling outliers, analysts can gain a deeper understanding of the data, improve the quality of analyses, and make more informed decisions. In this thesis, we propose a new measure for detecting outliers in univariate data. The new measure, called relative range, is defined as the range statistic divided by the interquartile range (IQR). Since the range provides a simple yet effective measure of data dispersion, analyzing the range distribution will help identify potential outliers that fall outside the expected range of values. The probability distribution of the relative range is estimated for both symmetrical and skewed data distributions using Monte Carlo simulations. Based on the estimated empirical distribution of the relative range, a threshold is determined and used to detect potential outliers. The thesis also proposes a sequential approach for outlier detection based on the relative range. In general, the relative range has shown to be a more robust statistic at detecting outliers in both sequential and non-sequential outlier detection.
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    Machine Learning Approach for Predicting Appointment No-Show in Healthcare
    (2024-07) Khalouf, Dana; Shamayleh, Abdulrahim; Alshariedeh, Hussam; Awad, Mahmoud
    The efficiency of public healthcare delivery is essential to achieving optimal health outcomes for patients. One factor that hinders efficiency is patient no-shows, which should be managed effectively to reduce its adverse impacts on patients and healthcare providers socially and economically. In particular, it reduces patient care access, underutilizes resources, and leads to lost revenue. A no-show, or missed appointment, happens when the outpatient does not attend the scheduled appointment or cancels it at short notice. It is a common challenge faced by several healthcare systems. Previous studies have considered different models to identify patients more likely to miss their appointments; however, no study analyzed appointment no-shows in the United Arab Emirates. Therefore, this study used a data analytics and machine learning approach to develop a classification model to predict whether an outpatient will miss their appointment in Dubai's primary healthcare clinics. While data analysis is applied to extract insights from historical data and identify the most useful features, machine learning tools extrapolate on historical data to generate future predictions. A historcial dataset of appointments for the period 2021-2022 is utilized in this study. A prediction accuracy of 78% and an AUC of 0.859 were achieved using Gradient Boosted Trees while optimizing on Youden’s Index. In addition, the most influential drivers of patient no-shows were identified from the feature importances produced from the tuned model and an extensive exploratory data analysis, which included the patient’s health plan, the clinic, and the patient’s weight. As a result, recommendations of startgeies were proposed to DHA clinics to reduce no-shows, which will improve efficiency and enhance patient access to care.
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    Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine
    (Sage, 2024) Shomope, Ibrahim; Percival, Kelly M.; Abdel-Jabbar, Nabil; Husseini, Ghaleb
    The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. Yet, direct comparisons of their predictive accuracy in modeling ultrasound-triggered drug release from liposomes remain limited. Existing studies predominantly focus on drug release under static conditions or with limited external stimuli rather than the dynamic, nonlinear responses observed under ultrasound exposure.Objective: This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm²). Methods: Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. Results: RF consistently outperformed SVM, achieving R² scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data. Conclusion: RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs.
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    Review of Gold Nanoparticles: Synthesis, Properties, Shapes, Cellular Uptake, Targeting, Release Mechanisms and Applications in Drug Delivery and Therapy
    (MDPI, 2024-10-16) Georgeous, Joel; AlSawaftah, Nour; Abuwatfa, Waad; Husseini, Ghaleb
    The remarkable versatility of gold nanoparticles (AuNPs) makes them innovative agents across various fields, including drug delivery, biosensing, catalysis, bioimaging, and vaccine development. This paper provides a detailed review of the important role of AuNPs in drug delivery and therapeutics. We begin by exploring traditional drug delivery systems (DDS), highlighting the role of nanoparticles in revolutionizing drug delivery techniques. We then describe the unique and intriguing properties of AuNPs that make them exceptional for drug delivery. Their shapes, functionalization, drug-loading bonds, targeting mechanisms, release mechanisms, therapeutic effects, and cellular uptake methods are discussed, along with relevant examples from the literature. Lastly, we present the drug delivery applications of AuNPs across various medical domains, including cancer, cardiovascular diseases, ocular diseases, and diabetes, with a focus on in vitro and in vivo cancer research.

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