Added value of systemic inflammation markers in predicting pulmonary infection in stroke patients

Zheng, Lv; Li, Wen; Wang, Lei; Zhang, Ning

doi:10.1097/md.0000000000028439

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…In evaluation the performance of classifier, the 30304 of dataset is split into 90% as training set and 10% for the testing set. This study chose logistic regression as an algorithm provided by scikit-learn that is recommended when dealing with supervised learning and as well in document classification problems (Schade & Schuhmacher, 2023;Zheng et al, 2021). The result of the performance of model algorithm for each feature is shown in Table 9.…”

Section: Resultsmentioning

confidence: 99%

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Mohd Jamaludin,

Majahar Ali,

Vun

et al. 2023

Mal. J. Fund. Appl. Sci.

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e-ticketing is one of the common applications used in technical support in Information Technology (IT) and has been used worldwide in any field of company. The benefits of e-ticketing can reduce the human efforts, increase the sufficiency of system and provides the benefits and efficiency to the customers. Also, e-ticketing ticketing system can enhance worker safety, improving productivity, increasing project efficiency and cause the good impact on the performance of the business in terms of profitability. The main objective of this study is defining the model performance in each important feature by analysing the complex dataset by using logistic regression as a Machine Learning (ML) algorithm. In evaluation the performance of classifier, the dataset is injected to Python programming and split into 90% as training set and 10% for the testing set. From the analysis, the study found that only 3 out of 11 independent features in dataset that are relevant chosen to proceed the ML analysis. From the result, the accuracy for sct_short_description, sct_cmdb_ci, and sct_assignment_group is 41.65%, 48.77% and 96.49%, respectively. It showed that the accuracy’s result for the sct_assignment_group resulted that the model is very good accuracy and indicate that the model is well performing. Meanwhile, the value of F1-score is 96.11% in each feature. This result indicates that the model has a good balance of precision and recall in its binary classification predictions. Hence, the study considers the sct_assignment_group as a best features to proceed the analysis. The future study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines and Naïve Bayes.

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Section: Resultsmentioning

confidence: 99%

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Mohd Jamaludin,

Majahar Ali,

Vun

et al. 2023

Mal. J. Fund. Appl. Sci.

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“…Furthermore, the average glucose level was selected as a high-impact risk factor. According to Zheng et al, the average glucose level is strongly associated with the occurrence of an ischemic stroke [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

et al. 2022

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Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients’ class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.

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Risk factors for pulmonary infection in elderly patients with acute stroke: A meta-analysis

You¹,

Bai²,

Wu³

et al. 2022

Heliyon

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Added value of systemic inflammation markers in predicting pulmonary infection in stroke patients

Cited by 4 publications

References 31 publications

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

Risk factors for pulmonary infection in elderly patients with acute stroke: A meta-analysis

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