Diagnosing thyroid disorders: Comparison of logistic regression and neural network models

Borzouei, Shiva; Mahjub, Hossein; Sajadi, Negar Asaad; Farhadian, Maryam

doi:10.4103/jfmpc.jfmpc_910_19

Cited by 21 publications

(11 citation statements)

References 18 publications

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“…This approach could make the prediction more flexible. ( Borzouei et al, 2020 ) If the selected input variables are sufficient and representative, and there is neither a closed correlation between them, the network could reveal their complex relationship and show the advantages in extrapolation. This view has been supported by Schonenberger, et al ( Schonenberger et al, 2020 ) Furthermore, this methodology has been applied to the prediction of the SARS epidemic by Bai and Jin (2005) ( Yanping Bai, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive model for severe COVID-19

Kang

Chen

Luo

et al. 2021

Infection, Genetics and Evolution

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Section: Introductionmentioning

confidence: 99%

Machine learning predictive model for severe COVID-19

Kang

Chen

Luo

et al. 2021

Infection, Genetics and Evolution

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“…Similar results were reported by several studies that utilized ANN in the prediction of outcomes. The ROC-AUCs of ANN vs. LRM were reported to be 0.77 vs. 0.72, 0.75 vs. 0.72, and 0.97 vs. 0.95 in predicting lower-back-pain progression, ICU mortality, and thyroid disease diagnosis, respectively [ 25 , 27 , 42 ]. Given that the less error prediction model is considered a healthcare researcher’s ultimate goal, the utilization of an artificial neural network can potentially provide new opportunities to obtain predictions with enhanced accuracy [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…The ROC-AUCs of ANN vs. LRM were reported to be 0.77 vs. 0.72, 0.75 vs. 0.72, and 0.97 vs. 0.95 in predicting lower-back-pain progression, ICU mortality, and thyroid disease diagnosis, respectively [ 25 , 27 , 42 ]. Given that the less error prediction model is considered a healthcare researcher’s ultimate goal, the utilization of an artificial neural network can potentially provide new opportunities to obtain predictions with enhanced accuracy [ 25 ]. This can explain the increasing trends of the utilization of ANN models making complex medical decisions and the prediction of outcomes in patients with various diseases [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, ANN models are considered a flexible, complex, non-linear modeling technique that possesses properties which are not found in other modeling systems, including a powerful performance in dealing patterns that are incomplete or noisy, and high fault tolerance [ 24 ]. Therefore, the artificial neural network is very useful when dealing with complex relationships and implicit interactions in the data [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Determinants of Inadequate Empiric Antimicrobial Therapy in ICU Sepsis Patients in Al-Madinah Al-Munawwarah, Saudi Arabia: A Comparison of Artificial Neural Network and Regression Analysis

et al. 2023

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In the management of sepsis, providing adequate empiric antimicrobial therapy is one of the most important pillars of sepsis management. Therefore, it is important to evaluate the adequacy of empiric antimicrobial therapy (EAMT) in sepsis patients admitted to intensive care units (ICU) and to identify the determinants of inadequate EAMT. The aim of this study was to evaluate the adequacy of empiric antimicrobial therapy in patients admitted to the ICU with sepsis or septic shock, and the determinants of inadequate EAMT. The data of patients admitted to the ICU units due to sepsis or septic shock in two tertiary healthcare facilities in Al-Madinah Al-Munawwarah were retrospectively reviewed. The current study used logistic regression analysis and artificial neural network (ANN) analysis to identify determinants of inadequate empiric antimicrobial therapy, and evaluated the performance of these two approaches in predicting the inadequacy of EAMT. The findings of this study showed that fifty-three per cent of patients received inadequate EAMT. Determinants for inadequate EAMT were APACHE II score, multidrug-resistance organism (MDRO) infections, surgical history (lower limb amputation), and comorbidity (coronary artery disease). ANN performed as well as or better than logistic regression in predicating inadequate EAMT, as the receiver operating characteristic area under the curve (ROC-AUC) of the ANN model was higher when compared with the logistic regression model (LRM): 0.895 vs. 0.854. In addition, the ANN model performed better than LRM in predicting inadequate EAMT in terms of classification accuracy. In addition, ANN analysis revealed that the most important determinants of EAMT adequacy were the APACHE II score and MDRO. In conclusion, more than half of the patients received inadequate EAMT. Determinants of inadequate EAMT were APACHE II score, MDRO infections, comorbidity, and surgical history. This provides valuable inputs to improve the prescription of empiric antimicrobials in Saudi Arabia going forward. In addition, our study demonstrated the potential utility of applying artificial neural network analysis in the prediction of outcomes in healthcare research.

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“…The two most prevalent thyroid illnesses, hyperthyroidism and hypothyroidism, were studied by the authors of 14 in order to make a diagnosis. A total of 310 cases were classified using the logical regression methodology, and the neural network method was used..…”

Section: Related Workmentioning

confidence: 99%