Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

Xu, Wenbo; Zhao, Yang; Nian, Shiyan; Feng, Lei; Bai, Xueling; Luo, Xianqi; Luo, Feng

doi:10.1177/0300060518777173

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…The massive outbreak of the COVID-19 and the continuous increased number of infected cases and mortalities have occurred while there is no effective treatment or vaccine ( 26 ). Thus, the health care systems around the world are imposing great challenges with severe shortages of required resources and exhaustion of the health care providers ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data

Shanbehzadeh

Kazemi-Arpanahi²,

Nopour

2021

mjiri

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Background: The novel 2019 Coronavirus disease (COVID-19) poses a great threat to global public health and the economy. The earlier detection of COVID-19 is the key to its treatment and mitigating the transmission of the virus. Given that Machine Learning (ML) could be potentially useful in COVID-19 identification, we compared 7 decision tree (DT) algorithms to select the best clinical diagnostic model. Methods: A hospital-based retrospective dataset was used to train the selected DT algorithms. The performance of DT models was measured using performance criteria, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and precision-recall curves (PRC). Finally, the best decision model was obtained based on comparing the mentioned performance criteria. Results: Based on the Gini Index (GI) scoring model, 13 diagnostic criteria, including the lung lesion existence (GI= 0217), fever (GI= 0.205), history of contact with suspected people (GI= 0.188), O 2 saturation rate in the blood (GI= 0.181), rhinorrhea (GI= 0.177), dyspnea (GI = 0.177), cough (GI = 0.159), history of taking the immunosuppressive drug (GI= 0.145), history of respiratory failure (ARDS) (GI= 0.141), lung lesion situation (GI= 0.133) and appearance (GI= 0.126), diarrhea (GI= 0.112), and nausea and vomiting (GI = 0.092) have been obtained as the most important criteria in diagnosing COVID-19. The results indicated that the J-48, with the accuracy= 0.85, F-Score= 0.85, ROC= 0.926, and PRC= 0.93, had the best performance for diagnosing COVID-19. Conclusion: According to the empirical results, it is promising to implement J-48 in health care settings to increase the accuracy and speed of COVID-19 diagnosis.

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Section: Discussionmentioning

confidence: 99%

Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data

Shanbehzadeh

Kazemi-Arpanahi²,

Nopour

2021

mjiri

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“…Due to the complexity and severity, it is currently difficult to evaluate the efficacy of psoriasis [7]. Although varied evaluation tools and models have been practiced to evaluate the efficacy of diseases, there are still some shortcomings including the inability to integrate multiple indicators to construct an evaluation system and the lack of impartiality in evaluating the weights of various indicators [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Evaluation System of Psoriasis Curative Effect Based on Bayesian Maximum Entropy Weight Self-Learning and Extended Set Pair Analysis

Kuai

Fei

Jiang

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Background. Psoriasis is a complex skin disease and difficult to evaluate, and this study aimed to provide an objective and systematic approach for evaluating the efficacy of psoriasis. Methods. We sought to construct a Bayesian network from sixteen indicators in four aspects of psoriasis (skin lesion conditions, laboratory indexes, quality of life, and accompanying symptoms) and obtained weights of each index by combining the analytic hierarchy process with maximum entropy self-learning. Furthermore, we adopted stability analysis to calculate the minimum sample size of the system. The extended set pair analysis was utilized to evaluate the efficacy based on improved weights, which overcomes the limitation of set pair analysis (unable to evaluate the efficacy with uncertain grades and thresholds). Results. A total of 100 psoriasis vulgaris patients were included to evaluate the curative effect by the system. We obtained the weights of each index and the Euclidean distance for efficacy evaluation of 100 patients. The sensitivity analysis proved that the results had no significant change with the variation of single patient’s indexes, which indicated that our results were stable to assess the effectiveness. Conclusions. We provided an available method of comprehensive effective evaluation of various indicators of psoriasis and based on both subjective and objective weights.

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“…Kanbayashi et al (2017) applied BLR for identifying and forecasting the risk factors for Varicella Zoster and Post Herpetic Neuralgia (PHN) after Hematopoietic Stem Cell Transplantation (HSCT). Furthermore, the importance of controlling factors influencing binary logistic regression analysis has been applied in the assessment of disease risk by Xu et al (2018). Surveying the literature demonstrates that few studies have been conducted on optimizing logistic regression models.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects)

Hashemi

Kamranrad

Bagheri

et al. 2020

IJMPB

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PurposeThe aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper.Design/methodology/approachThis paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) – a technique that assist to solve decision-making problems – and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm.FindingsThe results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using SA algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success.Practical implicationsThe application of the proposed method is illustrated via a real data set from energy projects.Originality/valueWe propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.

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Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

Cited by 19 publications

References 14 publications

Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data

Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data

A Novel Evaluation System of Psoriasis Curative Effect Based on Bayesian Maximum Entropy Weight Self-Learning and Extended Set Pair Analysis

A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects)

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