Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-05-17 DOI:10.1111/phn.13334

Seeta Devi, Rupali Gangarde, Shubhangi Deokar, Sayyed Faheemuddin Muqeemuddin, Sanidhya Rajendra Awasthi, Sameer Shekhar, Raghav Sonchhatra, Sonopant Joshi

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引用次数: 0

Abstract

Objectives: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS.

Design: The real-time data were collected from women presented at OPD of primary health centers (PHCs). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU-ROC for predicting non-attenders for CC.

Results: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99.

Conclusion: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening.

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通过分类、集合和深度学习模型预测未参加宫颈癌筛查的公共卫生护士的观点。

目的：妇女参加宫颈癌筛查（CCS）是社区医疗服务提供者关注的主要问题。本研究旨在利用各种算法，准确预测妇女不参加宫颈癌筛查的最大障碍：设计：从初级保健中心（PHC）门诊部就诊的妇女中收集实时数据。其中包括约 1046 名妇女参加和不参加社区保健服务的数据。在这项研究中，我们使用了分类模型、集合模型和深度学习模型三种模型，比较了预测未参加社区保健服务者的具体准确性和AU-ROC：目前的模型采用了 22 个预测因子，在集合模型中，软投票的特异性（96%）和灵敏度（93%）略高于加权平均。套袋法的准确率（98.49%）、特异性（97.3%）和理想灵敏度（100%）最高，AUC 为 0.99。分类模型显示，与逻辑回归相比，Naive Bayes 的特异性更高（97%），但灵敏度较低（91%）。随机森林和神经网络的准确率最高（98.49%），AUC 为 0.98。在深度学习中，与其他模型相比，LSTM 的准确率为 95.68%，特异性更高（97.60%），灵敏度较低（93.42%）。MLP 和 NN 的 AUC 值最高，达到 0.99：事实证明，采用集合模型和深度学习模型在预测不参加宫颈筛查的障碍方面最为有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.