Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. A systematic literature review (SLR) strategy is used in this study to give a comprehensive overview of the existing literature on forecasting data on thyroid disease diagnosed using ML. This study includes 168 articles published between 2013 and 2022, gathered from high-quality journals and applied meta-analysis. The thyroid disease diagnoses (TDD) category, techniques, applications, and solutions were among the many elements considered and researched when reviewing the 41 articles of cited literature used in this research. According to our SLR, the current technique's actual application and efficacy are constrained by several outstanding issues associated with imbalance. In TDD, the technique of ML increases data-driven decision-making. In the Meta-analysis, 168 documents have been processed, and 41 documents on TDD are included for observation analysis. The limits of ML that are discussed in the discussion sections may guide the direction of future research. Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future
The aim of the present study was to demonstrate the possible role of statins on the inflammatory biomarkers in patients with periodontal disease (PD) This cross-sectional study involved 74 patients with PD and/or dyslipidemia divided into Group A: 34 patients with PD (nonstatins users); Group B: 40 patients with PD (statins users); and Group C: 30 healthy controls. Total cholesterol (TC), triglyceride (TG) and high-density lipoprotein, C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and malondialdehyde (MDA) were measured . Blood pressure prolife and indices of PD were evaluated in each group. Statistical analysis was conducted by using SPSS version 20.0.
Aggression is a negative form of an anti-social behavior. It is produced because of a particular reason, desire, want, need, or due to the psychological state of the aggressor. It injures others physically or psychologically. Aggressive behaviors in human interactions cause discomfort and disharmony among interlocutors. The paper aims to identify how aggressive language manifests itself in the data under scrutiny in terms of the pragmatic paradigm. Two British literary works are the data; namely, Look Back in Anger by John Osborne (1956), and The Birthday Party by Harold Pinter (1957). This paper endeavors to answer the question of how aggressive language is represented in literature pragmatically? It is hoped to be significant to
... Show MoreSince more than a decade, human rights dialogue in the European Mediterranean Region has been marked by a number of tensions. Although a number of factors contribute to such disputes, the effect of human rights conditionality, which ties EU economic cooperation progression with partner countries human rights advancement, on the dialogue has not been studied. Understanding the aspects, impacts, and effects of conditionality on Euro-Med relations is crucial for furthering dialogue. Yet this variable has been almost entirely neglected in academic and policy research. The research concludes several direct and indirect impacts of conditionality on human rights dialogue using a mixed methodology approach. Direct effects are reflected in the wi
... Show MorePrevious studies on the synthesis and characterization of metal chelates with uracil by elemental analysis, conductivity, IR, UV-Vis, NMR spectroscopy, and thermal analysis were covered in this review article. Reviewing these studies, we found that uracil can be coordinated through the electron pair on the N1, N3, O2, or O4 atoms. If the uracil was a mono-dentate ligand, it will be coordinated by one of the following atoms: N1, N3 or O2. But if the uracil was bi-dentate ligand, it will be coordinated by atoms N1 and O2, N3 and O2 or N3 and O4. However, when uracil forms complexes in the form of polymers, coordination occurs through the following atoms: N1 and N3 or N1 and O4.
Background: Accurate detection of thyroid autoantibodies by enzyme linked immunosorbant assay technique namely thyroglobulin antibody, thyroid peroxides antibody is crucial in the differentiation of autoimmune thyroid disorders from other form of thyroid diseases.
Objective: Evaluation of the detection of thyroglobulin antibody and thyroid peroxides antibody in different thyroid diseases using enzyme linked immunosorbant assay technique.
Methods: - Seventy-five patients admitted to Surgical Units of Baghdad Medical City Hospital for the period between "October 2010 to June 2011" they were waiting to do thyroidectomy. They were chosen nonselectively for serological evaluation of above autoantibodies , and correlation of the results
Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy
... Show MoreAccurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector funct