Healthcare analytics (New York, N.Y.)最新文献_第3页

An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction 一种通过混合滤波和降维增强脑信号分类的分析框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-09 DOI: 10.1016/j.health.2025.100435

Rajani Rai B , Karunakara Rai B , Mamatha A S , Nikshitha

Accurate classification of focal and non-focal epilepsy is a critical healthcare analytics challenge that requires robust data preprocessing and feature optimization. This work develops an integrated analytics framework that combines hybrid filtering with hybrid dimensionality reduction to improve both signal quality and predictive performance. A multi-criteria ranking strategy based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed, incorporating conventional signal measures alongside distance and divergence metrics to identify optimal preprocessing pipelines. Statistical validation is performed using the Friedman test with Nemenyi post-hoc analysis to establish the significance of competing filter–dimensionality reduction combinations. The validated framework is benchmarked across conventional, hybrid, and deep learning classifiers, with the most effective configuration—Butterworth.

Wavelet Packet Decomposition (BW + WPD) filtering followed by Principal Component Analysis–Linear Discriminant Analysis (PCA + LDA)—achieving 95.63% accuracy using an Adaboost classifier on the Bern–Barcelona dataset. Evaluation on the independent Bonn dataset confirms robustness and cross-subject generalizability. These findings demonstrate the value of a multi-metric, statistically validated analytics strategy for reliable epilepsy detection, with potential applicability to broader healthcare signal classification tasks.

准确分类局灶性和非局灶性癫痫是一个关键的医疗保健分析挑战，需要稳健的数据预处理和特征优化。这项工作开发了一个集成的分析框架，将混合滤波与混合降维相结合，以提高信号质量和预测性能。采用基于TOPSIS （Order Preference by Similarity to Ideal Solution）的多准则排序策略，结合传统的信号度量以及距离和散度度量来确定最优的预处理管道。统计验证使用Friedman检验和Nemenyi事后分析来确定竞争过滤器降维组合的显著性。经过验证的框架在传统、混合和深度学习分类器中进行基准测试，并使用最有效的配置- butterworth。小波包分解（BW + WPD）滤波，然后是主成分分析-线性判别分析（PCA + LDA），在Bern-Barcelona数据集上使用Adaboost分类器实现95.63%的准确率。对独立波恩数据集的评估证实了鲁棒性和跨主题泛化性。这些发现证明了一种多度量的、经过统计验证的分析策略对可靠的癫痫检测的价值，具有更广泛的医疗信号分类任务的潜在适用性。

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引用次数: 0

An analytical review of blood supply chain management literature through science mapping and strategic diagrams 通过科学制图和战略图表对血液供应链管理文献进行分析回顾

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-07 DOI: 10.1016/j.health.2025.100433

Nurhadi Siswanto , Ivan Darma Wangsa , Ahmed Raecky Baihaqy , Patdono Suwignjo , Vincent F. Yu

The increasing complexity of healthcare systems and the critical role of blood supply chain (BSC) management in ensuring patient safety have motivated the need for a systematic synthesis of research in this domain. This study reviews the literature and presents a bibliometric and systematic literature review of BSC management studies. This study aimed to investigate the evolution of BSC management over twelve years (2014–2025). One hundred ninety-four published articles were retrieved from the Scopus database based on the inclusion criteria. Using bibliometric techniques, descriptive analysis was conducted to examine publication trends, citations, leading journals, influential authors, and contributing countries. Science mapping and strategic diagram analysis were employed to identify and visualize keyword networks, enabling the recognition of thematic clusters and their evolution. The results highlight five dominant research streams: donor engagement, demand forecasting, inventory and logistics optimization, resilience to disruptions, and the application of digital technologies such as artificial intelligence, machine learning, and blockchain. The analysis also reveals emerging sustainability and circular economy themes that remain underexplored, pointing to significant research gaps. This study contributes to theory by providing a structured knowledge map of BSC research and advancing understanding of its evolution. It offers practical insights for policymakers, blood banks, and healthcare managers to enhance the resilience, sustainability, and efficiency of BSC operations.

日益复杂的医疗系统和血液供应链（BSC）管理在确保患者安全方面的关键作用，促使需要在这一领域进行系统的综合研究。本研究回顾文献，对平衡记分卡管理研究进行文献计量学和系统的文献回顾。本研究旨在探讨平衡记分卡管理在过去12年（2014-2025）中的演变。根据纳入标准从Scopus数据库检索194篇已发表的文章。使用文献计量学技术，进行了描述性分析，以检查出版趋势、引用、主要期刊、有影响力的作者和贡献国家。利用科学制图和策略图分析对关键词网络进行识别和可视化，实现主题聚类及其演化的识别。结果突出了五个主要的研究流：捐助者参与、需求预测、库存和物流优化、中断恢复能力以及人工智能、机器学习和区块链等数字技术的应用。该分析还揭示了新兴的可持续性和循环经济主题仍未得到充分探索，指出了重大的研究空白。本研究通过提供平衡记分卡研究的结构化知识图谱和促进对其演变的理解，为理论做出了贡献。它为政策制定者、血库和医疗保健管理人员提供了实用的见解，以增强平衡计分卡运营的弹性、可持续性和效率。

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引用次数: 0

A log-linear analytics approach to cost model regularization for inpatient stays through diagnostic code merging 通过诊断代码合并的住院病人成本模型正则化的对数线性分析方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-06 DOI: 10.1016/j.health.2025.100431

Chi-Ken Lu, David Alonge, Nicole Richardson, Bruno Richard

Healthcare cost models that use a great number of detailed ICD-10 diagnostic codes produce unstable results, yet the underlying causes of this instability have not been well understood. This study provides a mathematical framework linking the variability of model coefficients to the uneven, power-law distribution of diagnostic codes and the structure of the regression model. We propose a transparent approach that improves coefficient stability by merging similar codes through hierarchical truncation. Using Medicare data, we demonstrate how this method clarifies the trade-off between code detail and model reliability, offering analysts and policymakers a practical and interpretable tool for diagnosis-based cost modeling.

使用大量详细的ICD-10诊断代码的医疗保健成本模型产生不稳定的结果，但这种不稳定的潜在原因尚未得到很好的理解。本研究提供了一个数学框架，将模型系数的可变性与诊断代码的不均匀幂律分布和回归模型的结构联系起来。我们提出了一种透明的方法，通过分层截断合并相似的代码来提高系数稳定性。使用医疗保险数据，我们展示了这种方法如何澄清代码细节和模型可靠性之间的权衡，为分析师和决策者提供了一种实用且可解释的工具，用于基于诊断的成本建模。

引用次数: 0

An interpretable machine learning model for dengue detection with clinical hematological data 基于临床血液学数据的登革热检测的可解释机器学习模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-03 DOI: 10.1016/j.health.2025.100430

Izaz Ahmmed Tuhin , A.K.M.Fazlul Kobir Siam , Md Mahfuzur Rahman Shanto , Md Rajib Mia , Imran Mahmud , Apurba Ghosh

Dengue fever remains a major global health concern that demands rapid and accurate diagnosis to prevent severe complications and support timely patient care. Traditional approaches relying on environmental variables often lack patient-level precision, limiting their clinical applicability. This study focuses on hematological parameters as more reliable indicators for early dengue detection. A novel machine learning framework, DengueStackX-19, was developed using 1,523 clinically verified patient records from Jamalpur 250-Bedded General Hospital, Jamalpur, Bangladesh. The dataset underwent rigorous preprocessing, normalization, and imbalance handling using various resampling techniques. Comparative evaluation across five balancing methods demonstrated that DengueStackX-19 consistently achieved the highest accuracy and robustness, performing effectively both before and after outlier removal. The model achieved 93.65 % accuracy and 89.63 % F1 during 10-fold cross-validation under SMOTEENN, and further attained 96.38 % accuracy and 94.20 % F1 in dengue classification, demonstrating robust generalization and consistent high performance across evaluation phases. Sensitivity analysis further verified its stability under feature perturbations. To ensure interpretability, SHAP and LIME were applied to identify the hematological factors most influential to the model's predictions, and the resulting patterns aligned with established clinical understanding. The model was deployed as an accessible web-based diagnostic tool, allowing healthcare professionals to perform real-time dengue detection without specialized laboratory infrastructure. This study demonstrates that hematology-driven AI models can significantly enhance diagnostic accuracy, reduce decision-making time, and improve patient outcomes, particularly in resource-limited settings.

登革热仍然是一个主要的全球卫生问题，需要迅速和准确的诊断，以防止严重并发症，并支持及时的病人护理。依赖环境变量的传统方法往往缺乏患者水平的精确性，限制了其临床适用性。本研究的重点是血液学参数作为早期登革热检测的更可靠指标。一种新的机器学习框架DengueStackX-19是利用来自孟加拉国Jamalpur 250床位综合医院的1,523例经临床验证的患者记录开发的。数据集经过严格的预处理、归一化和使用各种重采样技术的不平衡处理。五种平衡方法的对比评估表明，DengueStackX-19始终具有最高的准确性和鲁棒性，在异常值去除之前和之后都表现有效。在SMOTEENN下的10次交叉验证中，该模型的准确率为93.65%，F1为89.63%；在登革热分类中，该模型的准确率为96.38%，F1为94.20%，具有鲁棒的泛化性和跨评估阶段一致的高性能。灵敏度分析进一步验证了其在特征扰动下的稳定性。为了确保可解释性，应用SHAP和LIME来确定对模型预测影响最大的血液学因素，结果模式与已建立的临床理解一致。该模型作为一种可访问的基于网络的诊断工具部署，使卫生保健专业人员能够在没有专门实验室基础设施的情况下进行实时登革热检测。该研究表明，血液学驱动的人工智能模型可以显著提高诊断准确性，缩短决策时间，改善患者预后，特别是在资源有限的情况下。

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引用次数: 0

A bio-inspired approach to feature optimization for ischemic heart disease detection 缺血性心脏病检测特征优化的生物启发方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-30 DOI: 10.1016/j.health.2025.100427

D. Cenitta , N. Arul , T. Praveen Pai , R. Vijaya Arjunan , Tanuja Shailesh

Ischemic Heart Disease (IHD) stands as one of the primary contributors to worldwide deaths, therefore requiring precise and efficient predictive models. Standard machine learning techniques encounter hurdles, including excessive feature dimensions and unbalanced data distribution together with inappropriate feature group choice that negatively affect model effectiveness. The research introduces an optimized feature selection method by employing an Improved Squirrel Search Algorithm (ISSA) to raise the predictive capacity for IHD classification. The ISSA implements adaptive search features to automatically optimize feature selection, through which it maintains important attributes while eliminating redundant information. The selected features are evaluated using a Random Forest classifier, known for its robustness and interpretability in medical prediction tasks. Experimental results on the University of California Irvine (UCI) Heart Disease dataset show that the Improved Squirrel Search Algorithm–Random Forest (ISSA-RF) model achieves a classification accuracy of 98.12 %, outperforming existing feature selection techniques while reducing computational overhead. Bio-inspired optimization proves effective in medical diagnostics through recent research findings that lead to more efficient predictive healthcare models with interpretable properties.

缺血性心脏病（IHD）是全球死亡的主要原因之一，因此需要精确和有效的预测模型。标准的机器学习技术遇到了障碍，包括过多的特征维度和不平衡的数据分布，以及不适当的特征组选择，这些都会对模型的有效性产生负面影响。采用改进的松鼠搜索算法（ISSA）优化特征选择方法，提高IHD分类的预测能力。ISSA通过自适应搜索特征来自动优化特征选择，在保留重要属性的同时消除冗余信息。所选择的特征使用随机森林分类器进行评估，该分类器以其在医学预测任务中的鲁棒性和可解释性而闻名。在加州大学欧文分校（UCI）心脏病数据集上的实验结果表明，改进的松鼠搜索算法-随机森林（ISSA-RF）模型的分类准确率达到98.12%，优于现有的特征选择技术，同时减少了计算开销。通过最近的研究发现，以生物为灵感的优化在医学诊断中证明是有效的，这些发现导致了具有可解释属性的更有效的预测性医疗保健模型。

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引用次数: 0

An analytics framework for healthcare expenditure forecasting with machine learning 基于机器学习的医疗保健支出预测分析框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-28 DOI: 10.1016/j.health.2025.100428

John Wang , Shubin Xu , Yawei Wang , Houda EL Bouhissi

The United States healthcare system relies heavily on Medicaid, which serves nearly 80 million people and accounts for a substantial share of both state and federal budgets. This study employs a range of forecasting methods, including ARIMA, Holt's linear trend, polynomial regressions (degree 2 and 4), Prophet, and piecewise linear regression, as well as machine learning models such as random forest, gradient boosting, and support vector regression, to analyze the growth of Medicaid expenditures. Using data from 1966 to 2024, the analysis identifies historical patterns and evaluates model performance with Root Mean Squared Error (RMSE) and related metrics to project costs through 2035. The results show that the autoregressive model with integrated moving average and Prophet generate the most accurate baseline forecasts, suggesting that Medicaid expenditures are likely to exceed one trillion dollars within the next 15 years. Although the machine learning models produced somewhat lower estimates, they revealed complex relationships between policy variables and expenditure behavior, making them useful for building alternative forecasting scenarios. The discussion emphasizes the policy relevance of these findings, particularly in relation to budget sustainability and healthcare equity, and highlights the importance of employing multiple forecasting approaches. Overall, the study demonstrates the value of decision analytics in healthcare forecasting by highlighting the need for accurate predictions, flexible models, and interpretable outcomes. It provides evidence-based tools to anticipate Medicaid's financial challenges and support the development of sustainable healthcare strategies for the years ahead.

美国医疗保健系统严重依赖医疗补助计划，该计划为近8000万人提供服务，占州和联邦预算的很大一部分。本研究采用ARIMA、Holt线性趋势、多项式回归（2度和4度）、Prophet和分段线性回归等预测方法，以及随机森林、梯度增强和支持向量回归等机器学习模型，对医疗补助支出的增长进行了分析。使用1966年至2024年的数据，分析确定了历史模式，并使用均方根误差（RMSE）和2035年项目成本的相关指标评估了模型的性能。结果表明，综合移动平均线和Prophet的自回归模型产生了最准确的基线预测，表明医疗补助支出可能在未来15年内超过1万亿美元。尽管机器学习模型产生的估计值略低，但它们揭示了政策变量和支出行为之间的复杂关系，这使得它们对构建替代预测场景很有用。讨论强调了这些发现的政策相关性，特别是在预算可持续性和医疗公平方面，并强调了采用多种预测方法的重要性。总体而言，该研究通过强调对准确预测、灵活模型和可解释结果的需求，展示了决策分析在医疗保健预测中的价值。它提供了基于证据的工具来预测医疗补助计划的财务挑战，并支持未来几年可持续医疗保健战略的发展。

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引用次数: 0

An in-depth review and analysis of deep learning methods and applications in spinal cord imaging 深入回顾和分析深度学习方法及其在脊髓成像中的应用

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-28 DOI: 10.1016/j.health.2025.100429

Md Sabbir Hossain , Mostafijur Rahman , Mumtahina Ahmed , Ashifur Rahman , Md Mohsin Kabir , M.F. Mridha , Jungpil Shin

This systematic review explores the advances, technologies, and applications of deep learning in spinal cord magnetic resonance imaging (MRI). The current state of deep-learning techniques used for injury detection, disease diagnosis, and treatment planning in spinal cord imaging is thoroughly examined. This review includes a systematic analysis of over 100 studies from 2018 to 2025, selected based on clinical relevance, model performance, and innovation. Through a comprehensive analysis of recent literature, this review highlights the evolution and effectiveness of various deep-learning models in enhancing the accuracy and reliability of spinal cord MRI interpretations. Significant contributions of this review include identifying the most effective and innovative deep-learning approaches, such as Convolutional Neural Networks (CNNs) for precise lesion segmentation and Generative Adversarial Networks (GANs) for data augmentation. Additionally, it synthesizes current applications, such as improved injury detection and multiple sclerosis diagnosis, and explores deep-learning’s role in treatment planning. The review also addresses the challenges and limitations faced in this domain, including data scarcity, model interpretability, and computational demands, and proposes potential solutions and directions for future research. By offering these insights, this review provides a unique perspective on integrating deep-learning models into clinical workflows and their impact on clinical outcomes and patient care.

本系统综述探讨了脊髓磁共振成像（MRI）中深度学习的进展、技术和应用。深度学习技术用于损伤检测，疾病诊断和脊髓成像治疗计划的现状进行了彻底的检查。本综述包括对2018年至2025年的100多项研究的系统分析，这些研究是根据临床相关性、模型性能和创新来选择的。通过对近期文献的综合分析，本综述强调了各种深度学习模型在提高脊髓MRI解释的准确性和可靠性方面的发展和有效性。本综述的重要贡献包括确定最有效和创新的深度学习方法，例如用于精确病灶分割的卷积神经网络（cnn）和用于数据增强的生成对抗网络（gan）。此外，它还综合了当前的应用，如改进的损伤检测和多发性硬化症诊断，并探索了深度学习在治疗计划中的作用。本文还讨论了该领域面临的挑战和限制，包括数据稀缺性、模型可解释性和计算需求，并提出了未来研究的潜在解决方案和方向。通过提供这些见解，本综述为将深度学习模型集成到临床工作流程及其对临床结果和患者护理的影响提供了独特的视角。

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引用次数: 0

An analytical study of external factors influencing emergency occurrences in healthcare 影响医疗卫生突发事件发生的外部因素分析研究

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-19 DOI: 10.1016/j.health.2025.100426

Félicien Hêche , Philipp Schiller , Oussama Barakat , Thibaut Desmettre , Stephan Robert-Nicoud

This study investigates the impact of 19 external factors, related to weather, road traffic conditions, air quality, and time, on the hourly occurrence of emergencies. The analysis relies on six years of dispatch records (2015–2021) from the Centre Hospitalier Universitaire Vaudois (CHUV), which oversees 18 ambulance stations across the French-speaking region of Switzerland. First, classical statistical methods, including Chi-squared test, Student’s

t

-test, and information value, are employed to identify dependencies between the occurrence of emergencies and the considered parameters. Additionally, SHapley Additive exPlanations (SHAP) values and permutation importance are computed using eXtreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models. Training and hyperparameter optimization were performed on data from 2015–2020, while the 2021 data were held out for evaluation and for computing model interpretation metrics. Results indicate that temporal features – particularly the hour of the day – are the dominant drivers of emergency occurrences, whereas other external factors contribute minimally once temporal effects are accounted for. Subsequently, performance comparisons with a simplified model that considers only the hour of the day suggest that more complex machine learning approaches offer limited added value in this context. Operationally, this result supports the use of simple time-dependent demand curves for EMS planning. Such models can effectively guide staffing schedules and relocations without the overhead of integrating external data or maintaining complex pipelines. By highlighting the limited utility of external predictors, this study provides practical guidance for EMS organizations seeking efficient, data-driven resource allocation methods.

本研究考察了天气、道路交通状况、空气质量和时间等19个外部因素对每小时突发事件发生的影响。该分析基于瑞士沃杜瓦大学医院中心（CHUV） 6年（2015-2021年）的调度记录，该中心负责监管瑞士法语区18个救护站。首先，采用经典的统计方法，包括卡方检验、学生t检验和信息值，来确定突发事件的发生与所考虑的参数之间的依赖关系。此外，SHapley加性解释（SHAP）值和排列重要性使用极端梯度增强（XGBoost）和多层感知器（MLP）模型计算。对2015-2020年的数据进行训练和超参数优化，同时保留2021年的数据进行评估和计算模型解释指标。结果表明，时间特征——特别是一天中的时间——是紧急情况发生的主要驱动因素，而一旦考虑到时间影响，其他外部因素的作用就微乎其微。随后，与只考虑一天中的一个小时的简化模型的性能比较表明，在这种情况下，更复杂的机器学习方法提供的附加价值有限。从操作上讲，该结果支持使用简单的随时间变化的需求曲线进行EMS规划。这样的模型可以有效地指导人员安排和重新部署，而不需要集成外部数据或维护复杂的管道。通过强调外部预测因素的有限效用，本研究为EMS组织寻求有效的、数据驱动的资源分配方法提供了实用的指导。

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引用次数: 0

A machine learning framework for identifying phenotypes in chronic kidney disease 用于识别慢性肾脏疾病表型的机器学习框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-17 DOI: 10.1016/j.health.2025.100425

Marzieh Amiri Shahbazi , Mohammad Abdullah Al-Mamun , Todd Brothers , Imtiaz Ahmed

Identifying meaningful patient phenotypes is a cornerstone of data-driven healthcare, enabling risk stratification, resource allocation, and the design of personalized care strategies. Achieving this requires robust analytical methods that can uncover hidden structure in high-dimensional clinical data while ensuring stability and interpretability of results. In this study, we present a machine learning framework for phenotypic clustering that combines partition-based (

k

-means) and probabilistic (latent class analysis, LCA) approaches. By comparing subgroup assignments across these complementary methods, the framework provides an internal validation of clustering assignments. Rather than relying on a single method, the framework validates subgroup assignments through cross-method agreement, strengthening confidence in the robustness of the identified phenotypes and their utility for decision support. We apply the proposed framework to patients with chronic kidney disease (CKD) stratified by prior history of acute kidney injury (AKI), illustrating its value in uncovering population-level heterogeneity. While the mechanisms linking AKI to CKD phenotypic patterns remain poorly understood historically, this study investigates CKD trajectories in patients with and without prior AKI and identifies key phenotypic patterns. The analysis revealed consistent phenotypic structures, with over 80% agreement between the two clustering approaches. Distinct phenotypic patterns emerged between the AKI and non-AKI cohorts, with cardiovascular conditions consistently dominating in both groups. These findings demonstrate how stratified clustering can uncover risk signatures that traditional CKD staging systems may overlook. By combining complementary clustering algorithms, the framework strengthens the analytic foundation of phenotyping studies. Moreover, it enables the design of phenotype specific care pathways such as cluster aware monitoring panels and tailored coordination strategies, thus underscoring the broader potential of data-driven analytics to advance personalized medicine and healthcare decision support.

识别有意义的患者表型是数据驱动医疗保健的基石，可以实现风险分层、资源分配和个性化护理策略的设计。实现这一目标需要强大的分析方法，可以揭示高维临床数据中的隐藏结构，同时确保结果的稳定性和可解释性。在本研究中，我们提出了一种用于表型聚类的机器学习框架，该框架结合了基于分区（k-means）和概率（潜类分析，LCA）方法。通过比较这些互补方法中的子组分配，该框架提供了聚类分配的内部验证。该框架不是依赖于单一方法，而是通过跨方法协议验证子组分配，增强了对已识别表型的稳健性及其决策支持效用的信心。我们将提出的框架应用于按急性肾损伤（AKI）病史分层的慢性肾脏疾病（CKD）患者，说明其在揭示人群水平异质性方面的价值。虽然AKI与CKD表型模式之间的联系机制在历史上仍然知之甚少，但本研究调查了有和没有AKI的患者的CKD轨迹，并确定了关键的表型模式。分析揭示了一致的表型结构，两种聚类方法之间的一致性超过80%。在AKI和非AKI组之间出现了不同的表型模式，心血管疾病在两组中始终占主导地位。这些发现证明了分层聚类如何揭示传统CKD分期系统可能忽略的风险特征。通过结合互补聚类算法，该框架加强了表型研究的分析基础。此外，它能够设计特定表型的护理途径，如集群感知监测面板和量身定制的协调策略，从而强调数据驱动分析在推进个性化医疗和医疗保健决策支持方面的更广泛潜力。

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引用次数: 0

An explainable analytics framework for predicting diabetes in women using Convolutional Neural Networks 使用卷积神经网络预测女性糖尿病的可解释分析框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-10-10 DOI: 10.1016/j.health.2025.100422

Gazi Mohammad Imdadul Alam , Tapu Biswas , Sharia Arfin Tanim , M.F. Mridha

Diabetes is a chronic metabolic disorder that heightens the risk of complications for women and presents diagnostic challenges owing to imbalanced datasets and the need for interpretable predictive models. In this study, we propose a 1D Convolutional Neural Network (1D CNN) model that achieves an accuracy of 98.61% on German Patient Dataset, comprising 2,000 samples, and 99.35% on the Bangladeshi Patient Dataset, which includes 465 samples. Our model effectively addresses class imbalance by integrating the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE-ENN), which significantly enhances performance. Additionally, we conducted a statistical comparison with Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, demonstrating our CNN’s superior accuracy while maintaining reduced complexity and enhanced transparency through the integration of SHapley Additive exPlanations (SHAP). Our SHAP analysis revealed significant variations in feature importance between the two populations, offering culturally relevant insights into the risk factors for diabetes. The SHAP analysis not only facilitates interpretability by allowing healthcare professionals to understand the influence of individual features but also emphasizes the cultural context of diabetes risk. Overall, our findings surpass existing methodologies in terms of accuracy and complexity while underscoring the critical need for demographic diversity in predictive healthcare models, paving the way for more effective diabetes prediction strategies.

糖尿病是一种慢性代谢紊乱，增加了女性并发症的风险，由于数据集不平衡和需要可解释的预测模型，糖尿病给诊断带来了挑战。在这项研究中，我们提出了一个一维卷积神经网络（1D CNN）模型，该模型在德国患者数据集（包括2000个样本）上实现了98.61%的准确率，在孟加拉国患者数据集（包括465个样本）上实现了99.35%的准确率。我们的模型通过集成合成少数过采样技术和编辑最近邻（SMOTE-ENN）有效地解决了类不平衡问题，显著提高了性能。此外，我们与多层感知器（MLP）、长短期记忆（LSTM）和双向LSTM （BiLSTM）模型进行了统计比较，证明了我们的CNN在通过集成SHapley加性解释（SHAP）保持降低复杂性和增强透明度的同时具有卓越的准确性。我们的SHAP分析揭示了两种人群在特征重要性上的显著差异，为糖尿病的危险因素提供了与文化相关的见解。SHAP分析不仅有助于医疗保健专业人员理解个体特征的影响，而且还强调了糖尿病风险的文化背景。总的来说，我们的研究结果在准确性和复杂性方面超越了现有的方法，同时强调了预测医疗模型中人口多样性的关键需求，为更有效的糖尿病预测策略铺平了道路。

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引用次数: 0