Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania

Tarimo, Clifford Silver; Bhuyan, Soumitra S.; Zhao, Yi; Ren, Weicun; Mohammed, Akram; Li, Quanman; Gardner, Marilyn M.; Mahande, Michael Johnson; Wang, Yuhui; Wu, Jian

doi:10.1186/s12884-022-04534-0

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…In another study conducted by Tarimo et al (2022), the researchers developed an ensemble classification algorithm consisting of Adaboost, Gradient Boosting, and Extreme Gradient Boosting (Adaboost, Gboost, and XGBoost). Data processing and prediction were performed using the Python programming language.…”

Section: Frequently Modeled Conventional Ensemble Classifiermentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

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Section: Frequently Modeled Conventional Ensemble Classifiermentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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“…This initial assessment, conducted at one and five minutes post-delivery, is vital for identifying newborns requiring immediate medical attention (AAPCFN, 2015). Despite its widespread acceptance and utilization globally, the interpretation and application of the Apgar score by healthcare professionals exhibit significant variability, potentially impacting neonatal outcomes (Tarimo et al, 2022). This variability underscores a crucial gap in neonatal care, particularly in settings with limited resources or where continuous medical education might be intermittent (Corman et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Apgar score comprehension in clinical practice: A cross-sectional study of healthcare professionals in Dhaka, Bangladesh

Most Nasrin Khatun,

Tania Akter,

Habiba Benta Hasan

et al. 2024

World J. Adv. Res. Rev.

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Introduction: The Apgar score is a critical tool for assessing the health of newborns immediately after birth, with implications for immediate clinical care decisions. Despite its established importance, variations in the comprehension and application of the Apgar score among healthcare professionals can significantly affect neonatal outcomes. Objective: This cross-sectional study aimed to assess the comprehension and application of the Apgar score among healthcare professionals in Dhaka, Bangladesh, to identify gaps in knowledge and practice that could impact neonatal care outcomes. Methods: Conducted between September and December 2022 at a tertiary medical college and hospital in Dhaka, this study involved 80 nurses from the labor room, selected through purposive sampling. Data were collected via face-to-face interviews using a pre-tested semi-structured questionnaire, with knowledge assessed on a 24-point scale and categorized as good (>15) or poor (<15). Statistical analysis was performed using SPSS 26.0. Results: Most respondents (31.78% aged 27-33 years and 32.63% aged 34-39 years) were nursing college graduates (40.69%). A significant knowledge gap was identified, with only 25.33% demonstrating a good level of understanding of the APGAR score. Notable discrepancies were found in the recognition of the Apgar score's components, criteria, and clinical implications. Educational level significantly influenced Apgar score knowledge (p=0.046), with nursing college graduates showing greater understanding. Recent training appeared to impact knowledge positively, suggesting the need for continuous professional development. Conclusion: The study highlights a concerning gap in the understanding and application of the Apgar score among healthcare professionals in Dhaka, emphasizing the need for enhanced neonatal care education and training. Addressing these gaps through comprehensive educational programs and continuous professional development is crucial for improving neonatal assessment practices and outcomes in Bangladesh.

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A Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning Algorithms

Shaambhavi,

Bhavani

2024

Lecture Notes in Electrical Engineering

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Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania

Cited by 3 publications

References 67 publications

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Apgar score comprehension in clinical practice: A cross-sectional study of healthcare professionals in Dhaka, Bangladesh

A Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning Algorithms

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