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bsj-8544
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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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

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Publication Date
Fri Oct 23 2020
Journal Name
Medico-legal Update
Pro- and anti-inflammatory Cytokines in Coronavirus Disease 2019
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Publication Date
Fri Jan 15 2021
Journal Name
Frontiers In Cardiovascular Medicine
Association Between Periodontal Disease and Atherosclerotic Cardiovascular Diseases: Revisited
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Atherosclerotic cardiovascular disease (ACVD) is an inflammatory disease of the coronary arteries associated with atheroma formation, which can cause disability and often death. Periodontitis is ranked as the sixth most prevalent disease affecting humans affecting 740 million people worldwide. In the last few decades, researchers have focused on the effect of periodontal disease (PD) on cardiovascular disease. The aim of this review was to investigate the association between these two diseases. PD is a potential risk factor that may initiate the development, maturation, and instability of atheroma in the arteries. Two mechanisms were proposed to explain such association, either periodontal pathogens directly invade bloodstream or in

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Publication Date
Mon Apr 11 2011
Journal Name
Icgst
Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Publication Date
Fri Jul 12 2013
Journal Name
Journal Of Biosciences
A functional connection of Dictyostelium paracaspase with the contractile vacuole and a possible partner of the vacuolar proton ATPase
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Publication Date
Wed Feb 05 2020
Journal Name
Journal Of Physical Education
The Effect of Group Investigation Model on Learning overhead and underarm Pass in Volleyball
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Volleyball is one of the sports that require physical and skill abilities thus many teaching models appeared to teach these abilities like group investigation model. The research aimed at identifying the effect of group investigation model on learning underarm and overhead passing in volleyball. The researchers hypothesized statistical differences between pre and posttests in learning underarm and overhead passing in volleyball as well as differences in posttests of controlling and experimental groups in learning underarm and overhead passing in volleyball. The researcher used the experimental method on (30) second year female students of physical education and sport sciences college/ university of Baghdad. Group investigation model was app

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Publication Date
Sun Jan 01 2017
Journal Name
National Journal Of Physiology, Pharmacy And Pharmacology
The attitudes of final year medical and pharmacy students to interprofessional learning in Iraq
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Publication Date
Sun Jul 06 2014
Journal Name
Journal Of Educational And Psychological Researches
The influence of Online Training Courses on Iraqi EFL Instructors Teaching and Learning Process
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For over a decade, educational technology has been used sparingly in our schools and universities. Online training courses have been used since 2003 to fill the gaps in our learning system and to add extra program besides classroom learning. This paper aims to investigate the Iraqi EFL instructors’ participating in online training courses and its influence on the process of teaching and learning.

       The sample of present study consists of 30 instructors from University of Baghdad. The questionnaire of sixteen items was constructed. After ensuring validity and reliability of questionnaire, it was applied on March 2013 and the result shows that most of instructors improve their teaching methods b

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Publication Date
Mon Jul 08 2024
Journal Name
Journal Of Microbiology, Biotechnology And Food Sciences
SYNERGISTIC EFFECTS OF NEEM OIL AND GENTAMICIN ON PSEUDOMONAS AERUGINOSA VIA PHZM GENE DOWNREGULATION: A COMPREHENSIVE REVIEW
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Exploring the antibacterial potential of neem oil (Azadirachta indica) in combination with gentamicin (GEN) against pathogenic molds, especially Pseudomonas aeruginosa, has drawn concern due to the quest for natural treatment options against incurable diseases. Prospective research directions include looking for natural cures for many of the currently incurable diseases available now. microbial identification system, were used to identify the isolates. The research utilized a range of methods, such as the diffusion agar well (AWD) assays, TEM (transmission electron microscopy) analysis, minimum inhibitory concentration (MIC) assays, and real-time PCR (RT-qPCR) to analyze bacterial expression and the antibacterial action of neem oil (Azadira

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Publication Date
Wed Jan 01 2025
Journal Name
International Journal Of Hydrogen Energy
A comprehensive review of battery thermal management systems for electric vehicles: Enhancing performance, sustainability, and future trends
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Publication Date
Sat Jan 31 2026
Journal Name
Wasit Journal Of Sports Sciences
The impact of the Needham model on learning the skills of dribbling and handling in football for students
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