Results in Control and Optimization最新文献

Modelling lymphatic filariasis dynamics using Levenberg–Marquardt algorithm-artificial neural networks 利用Levenberg-Marquardt算法-人工神经网络建立淋巴丝虫病动力学模型

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-15 DOI: 10.1016/j.rico.2026.100659

Mussa A. Stephano , John N. Mlyahilu , Il Hyo Jung

This paper introduces a hybrid Levenberg–Marquard-Artificial Neural Network (LMA-ANN) framework for modeling the complex transmission dynamics of lymphatic filariasis (LF), a debilitating vector-borne neglected tropical disease. The methodology addresses key challenges in data-driven epidemiological forecasting by combining the fast convergence properties of the Levenberg–Marquardt optimization algorithm with the universal function approximation capability of neural networks. We evaluate the proposed framework against four established neural architectures such as Multilayer Perceptron (MLP), Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) using both pristine and Gaussian noise-augmented synthetic datasets generated from a compartmental epidemiological model solved with a high-fidelity Runge–Kutta method. Results demonstrate that the LMA-ANN achieves superior predictive accuracy, with the lowest error metrics of

M A E = 0.029, R M S E = 0.039, M S E = 0.0015

and highest coefficient of determination of

R^{2} = 0.990

on noise-augmented data, while maintaining computational efficiency with the shortest training of

87.4 s

and inference of

2.9 ms

times. Crucially, the CNN and RNN architectures exhibited worst performance degradation on the noise-augmented dataset, yielding negative

R^{2}

values of

- 0.15

and

- 0.42

respectively, indicating predictions worse than a simple mean model. This highlights a critical limitation of complex architectures when trained on limited, noisy epidemiological data. The study provides two principal contributions: (1) a robust, computationally efficient LMA-ANN framework that accurately captures LF dynamics under realistic data constraints, and (2) evidence-based guidance for model selection in epidemiological applications, emphasizing that architectural complexity must be carefully matched with data quality and quantity. These findings advance computational methods for infectious disease modeling and offer a generalizable tool for public health decision-making in resource-limited settings.

本文介绍了一种混合levenberg - marquard -人工神经网络（LMA-ANN）框架，用于模拟淋巴丝虫病（LF）的复杂传播动力学，LF是一种由媒介传播的被忽视的热带疾病。该方法通过将Levenberg-Marquardt优化算法的快速收敛特性与神经网络的通用函数逼近能力相结合，解决了数据驱动流行病学预测中的关键挑战。我们使用原始和高斯噪声增强的合成数据集对四种已建立的神经结构(多层感知器（MLP）、全连接神经网络（FCNN）、卷积神经网络（CNN）和循环神经网络（RNN）进行了评估，这些数据集是由用高保真龙格-库塔方法求解的分区流行病学模型生成的。结果表明，LMA-ANN在噪声增强数据上取得了较好的预测精度，最低误差指标MAE=0.029，RMSE=0.039，MSE=0.0015，最高决定系数R2=0.990，同时保持了计算效率，最短训练时间为874秒，推理时间为2.9ms。关键是，CNN和RNN架构在噪声增强数据集上表现出最严重的性能下降，分别产生负R2值- 0.15和- 0.42，表明预测比简单的平均模型更差。这突出了复杂架构在有限的、嘈杂的流行病学数据上训练时的一个关键局限性。该研究提供了两个主要贡献：(1)一个鲁棒的、计算效率高的LMA-ANN框架，可以在现实数据约束下准确捕获LF动态；(2)为流行病学应用中的模型选择提供循证指导，强调架构复杂性必须与数据质量和数量仔细匹配。这些发现促进了传染病建模的计算方法，并为资源有限环境下的公共卫生决策提供了一种通用工具。

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引用次数: 0

Optimal transport and incentive design in multi-agent economic control 多主体经济控制下的最优运输与激励设计

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-12 DOI: 10.1016/j.rico.2026.100656

Ramen Ghosh

This paper develops a principled framework for incentive design in multi-agent economic systems using tools from optimal transport (OT) theory and decentralized control. We consider a class of stochastic multi-agent environments in which each agent selects actions to minimize individual cost functions that depend on both private preferences and aggregate outcomes. To promote socially desirable allocations, we introduce an OT-based mechanism design approach, where incentives are computed as gradients of a Lagrangian dual formulation over probability measures. Our main results establish: (i) a KKT-type characterization of incentive compatibility in Wasserstein space, (ii) monotonicity and fairness of equilibrium allocations under convex coupling, (iii) structural convexity of cost functionals over coupled agent dynamics, (iv) convergence of iterative market updates to optimal allocations, and (v) efficiency guarantees under decentralized feedback. We demonstrate that fairness and incentive alignment emerge naturally as solutions to constrained OT problems, allowing for scalable, interpretable, and robust economic control policies. This formulation provides a unifying perspective on decentralized optimization, mechanism design, and ergodic fairness in economic networks, and opens new directions for data-driven social planning under uncertainty.

本文利用最优运输理论和分散控制理论，建立了多智能体经济系统激励设计的原则框架。我们考虑了一类随机多智能体环境，其中每个智能体选择行动以最小化依赖于私人偏好和总结果的个体成本函数。为了促进社会理想的分配，我们引入了一种基于ot的机制设计方法，其中激励被计算为拉格朗日对偶公式在概率度量上的梯度。我们的主要研究结果建立了：(i) Wasserstein空间中激励相容性的kkt型表征，（ii）凸耦合下均衡分配的单调性和公平性，（iii）耦合agent动力学上成本函数的结构凸性，（iv）迭代市场更新对最优分配的收敛性，以及(v)分散反馈下的效率保证。我们证明，公平和激励一致性作为受限OT问题的解决方案自然出现，允许可扩展、可解释和稳健的经济控制政策。这一表述为经济网络中的分散优化、机制设计和遍历公平提供了统一的视角，为不确定条件下数据驱动的社会规划开辟了新的方向。

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引用次数: 0

Optimizing epidemic control: Nash game approach to stochastic modeling with Brownian motion 优化流行病控制：布朗运动随机建模的纳什博弈方法

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-07 DOI: 10.1016/j.rico.2026.100657

Md. Abdullah Bin Masud , Sharmina Rahman , Faijun Nesa Shimi , Mostak Ahmed , Rathindra Chandra Gope

This research expands upon stochastic modeling for COVID-19 management by incorporating Nash game theory to optimize control strategies for disease transmission. Building on our original model, which has integrated Brownian motion and nonlinear dynamics to enhance diagnosis and isolation procedures, we now apply Nash game theory to explore the interactions between multiple control variables. Using Pontryagin’s Maximum Principle for theoretical analysis and MATLAB and Python for numerical simulations, we demonstrate that Nash control offers a more practical approach than traditional game theory for balancing interventions. The model’s performance, validated with Worldometer data, achieves low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), underscoring its high predictive accuracy. Our findings suggest that Nash control provides a superior framework for real-time epidemic management by optimizing disease control policies, particularly when coordinating antiviral treatments and isolation measures. This work highlights the advantages of Nash-based strategies in developing robust and adaptive epidemic management systems.

本研究扩展了新冠肺炎管理的随机建模，并结合纳什博弈论优化疾病传播控制策略。在我们的原始模型的基础上，我们整合了布朗运动和非线性动力学来增强诊断和隔离程序，我们现在应用纳什博弈论来探索多个控制变量之间的相互作用。使用庞特里亚金的极大值原理进行理论分析，使用MATLAB和Python进行数值模拟，我们证明纳什控制提供了比传统博弈论更实用的方法来平衡干预。该模型的性能通过Worldometer数据验证，实现了较低的均方根误差（RMSE）和平均绝对百分比误差（MAPE），强调了其较高的预测精度。我们的研究结果表明，纳什控制通过优化疾病控制政策，特别是在协调抗病毒治疗和隔离措施时，为实时流行病管理提供了一个优越的框架。这项工作突出了基于纳什的策略在开发健壮和适应性强的流行病管理系统方面的优势。

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引用次数: 0

Stability and weighted l2-gain analysis of discrete-time switched T–S fuzzy systems based on admissible-edge-dependent weighted average dwell time strategy 基于允许边相关加权平均停留时间策略的离散时间切换T-S模糊系统稳定性及加权12增益分析

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-05 DOI: 10.1016/j.rico.2026.100655

Qiang Yu, Lijuan Mao

The paper introduces the admissible-edge-dependent weighted average dwell time switching strategy that not only considers the differences and compensation between subsystems, but also takes into account the switching order of subsystems. The global uniform asymptotic stability and weighted

l_{2}

-gain of a class of discrete-time switched nonlinear systems and its related switched T–S (Takagi–Sugeno) model are studied under the new strategy and the multiple discontinuous Lyapunov function approach. The obtained results present a larger feasible range of switching signals than the existing results. Finally, a numerical example is given to illustrate the validity and superiority of the results.

提出了一种基于允许边的加权平均停留时间切换策略，该策略不仅考虑了子系统间的差异和补偿，而且考虑了子系统间的切换顺序。研究了一类离散时间切换非线性系统及其相关的切换T-S （Takagi-Sugeno）模型在新策略和多重不连续Lyapunov函数方法下的全局一致渐近稳定性和加权12增益。所得结果比现有结果提供了更大的开关信号可行范围。最后通过数值算例说明了所得结果的有效性和优越性。

引用次数: 0

Image classification and object detection complexity optimization: Exploring deep learning models on camera trap and surveillance clips 图像分类和目标检测复杂性优化：探索摄像机陷阱和监控片段的深度学习模型

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-03 DOI: 10.1016/j.rico.2026.100654

Hayder Yousif , Zahraa Al-Milaji

Input image size for convolutional neural networks (CNNs) has played a major role in classification accuracy and network speed. Designing a large depth, scale, and resolution CNN model cannot guarantee the best performance because of the problems of overfitting and memorization. On the other hand, object detection models have produced very low performance on event-triggered camera-trap images due to highly dynamic scenes. In this paper, we propose a framework for optimizing image classification in terms of performance and complexity by selecting the convenient deep learning model for each image. Based on the image sequence activation maps, we propose Resolution Selection Model (RSM) that generates a weight value for each image in the sequence. We utilize support vector machine (SVM) and the generated weight from RSM to select the appropriate deep learning model. We utilized EfficientNet models that have different input image resolutions to classify and detect the objects from the scaled images. Our results on camera-trap and surveillance images show the efficacy of the proposed method compared to the state-of-the-art architectures in terms of accuracy and computational complexity.

卷积神经网络（cnn）的输入图像大小对分类精度和网络速度起着重要作用。设计一个大深度、大尺度、大分辨率的CNN模型，由于存在过拟合和记忆问题，无法保证最佳的性能。另一方面，由于高度动态的场景，物体检测模型在事件触发的相机陷阱图像上产生了非常低的性能。在本文中，我们提出了一个框架，通过为每个图像选择方便的深度学习模型，从性能和复杂性方面优化图像分类。基于图像序列激活映射，我们提出了分辨率选择模型（RSM），该模型为序列中的每个图像生成一个权重值。我们利用支持向量机（SVM）和RSM生成的权值来选择合适的深度学习模型。我们利用具有不同输入图像分辨率的effentnet模型从缩放图像中对目标进行分类和检测。我们在摄像机陷阱和监控图像上的结果表明，与最先进的架构相比，所提出的方法在准确性和计算复杂性方面是有效的。

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引用次数: 0

Stability and optimizing the treatment control of tuberculosis model via numerical approach 用数值方法研究结核模型的稳定性和治疗控制的优化

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-26 DOI: 10.1016/j.rico.2025.100650

Muhammad Farman , David Amilo , Manal Ghannam , Kottakkaran Sooppy Nisar , Mohamed Hafez

According to World Health Organization data, tuberculosis (TB) affects nearly one-third of the world’s population and causes several million deaths and new cases each year. Recent advances in fractal–fractional differential operators have proven effective in simulating complex real-world problems. In this study, we present a TB model with an emphasis on hospital treatment and public health education, using a fractal–fractional operator under the Mittag-Leffler function. The study focuses on biological feasibility elements such as unique solutions, existence, positivity, and feasible domains. The Lipschitz and growth conditions are used to demonstrate the existence and uniqueness of solutions to the proposed TB system. A next-generation matrix technique is used to calculate the effective reproductive number of tuberculosis to determine its spread. Suitable Lyapunov functionals are developed to demonstrate the global stability of both TB-free and endemic equilibria. Each model parameter’s impact on the effective reproductive number is assessed using a normalized sensitivity index calculation. A numerical iterative method with Newton polynomial interpolation is utilized to demonstrate the usefulness of the proposed model, and numerical simulations show that it is more efficient at various fractional orders. We looked at numerical data from a variety of factors and fractional order values, concentrating on their impact on disease eradication. The simulation results are compared between the Newton polynomial interpolation approach and the fractional Adams–Bashforth–Moulton predictor–corrector method for the model compartments. The fractal–fractional approach essentially combines the complex real-world dynamics of infectious diseases with theoretical mathematics. This approach offers deep insights that help improve public health decision-making and guide successful control measures.

根据世界卫生组织的数据，结核病（TB）影响着世界近三分之一的人口，每年造成数百万人死亡和新病例。分形-分数阶微分算子的最新进展已被证明在模拟复杂的现实问题方面是有效的。在这项研究中，我们提出了一个结核病模型，重点是医院治疗和公共卫生教育，在Mittag-Leffler函数下使用分形-分数算子。研究的重点是生物可行性要素，如唯一解、存在性、正性和可行域。利用Lipschitz条件和生长条件证明了所提出的TB系统解的存在性和唯一性。采用新一代基质技术计算结核的有效繁殖数，以确定其传播。开发了合适的Lyapunov泛函来证明无结核病和地方性平衡的全局稳定性。每个模型参数对有效繁殖数的影响采用归一化敏感性指数计算进行评估。利用牛顿多项式插值的数值迭代方法验证了该模型的有效性，数值仿真结果表明，该模型在不同分数阶上都具有较高的效率。我们查看了来自各种因素和分数阶值的数值数据，重点关注它们对疾病根除的影响。比较了牛顿多项式插值法和分数阶Adams-Bashforth-Moulton预测校正法对模型室的模拟结果。分形-分数方法本质上是将复杂的真实世界传染病动力学与理论数学相结合。这种方法提供了深刻的见解，有助于改进公共卫生决策并指导成功的控制措施。

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引用次数: 0

Optimal fractional order PI controller design for time-delayed processes 时滞过程的最优分数阶PI控制器设计

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-22 DOI: 10.1016/j.rico.2025.100651

Erdal Cokmez, Ibrahim Kaya

The usage of fractional calculus enables additional flexibility and precision regarding the control parameters. This study introduces a fully analytical design for a fractional-order Proportional-Integral (FOPI) controller, eliminating the need for predefined parameters or iterative optimization. The Åström recursive algorithm, previously applied to integer-order controllers, is adapted for the first time to optimize FOPI controllers based on integral of squared time error (ISTE), integral of squared time-squared error (IST²E), and integral of squared time-cubed error (IST³E) performance criteria. This study differs from others since instead of specific types, it combines three types of first-order plus time-delay processes: stable (SFOPTD), integrating (IFOPTD), and unstable (UFOPTD) processes. Analytical formulas have been derived for optimal parameter selection, while separate formulas provided for gain margin (GM), phase margin (PM), and maximum sensitivity (Ms) enable the pre-determination of system robustness. The controller's performance is validated using simulations of the step response, disturbance rejection, control effort, and perturbation response. In addition, real-time experiments on an inverted pendulum illustrate its utility in dynamic processes. This provides a comprehensive framework aimed to further the development of fractional-order control by providing a systematic solution to a large scope of industrial applications.

分数阶微积分的使用使控制参数具有额外的灵活性和精度。本研究介绍了分数阶比例积分（FOPI）控制器的完全解析设计，消除了预定义参数或迭代优化的需要。先前应用于整阶控制器的Åström递归算法首次适用于基于时间误差平方积分（ISTE）、时间误差平方积分（IST2E）和时间立方误差平方积分（IST3E）性能标准的FOPI控制器优化。本研究与其他研究的不同之处在于，它结合了三种一阶加时滞过程：稳定（SFOPTD）、整合（IFOPTD）和不稳定（UFOPTD）过程，而不是特定的类型。推导了最佳参数选择的解析公式，同时提供了增益裕度（GM），相位裕度（PM）和最大灵敏度（Ms）的单独公式，可以预先确定系统的鲁棒性。通过阶跃响应、扰动抑制、控制努力和扰动响应的仿真验证了控制器的性能。另外，在倒立摆上进行了实时实验，说明了该方法在动态过程中的应用。这提供了一个全面的框架，旨在通过为大范围的工业应用提供系统的解决方案，进一步发展分数阶控制。

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引用次数: 0

An innovative two-stage method for balancing time, cost, quality, and additional objectives in multi-mode resource-constrained project scheduling: A case study of a food product packaging production line project 在多模式资源约束项目调度中平衡时间、成本、质量和附加目标的创新两阶段方法：以食品包装生产线项目为例研究

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-19 DOI: 10.1016/j.rico.2025.100649

Hossein Edrisi, Meghdad Jahromi, Narges Norouzi

This paper presents a novel two-stage approach for optimizing the scheduling of multi-objective construction projects, validated through a real-world case study of a food product packaging production line. Beyond the conventional objectives of time, cost, and quality, five additional factors—risk, scope creep, environmental impacts, stakeholder satisfaction, and safety—are quantitatively incorporated based on expert judgment and normalized performance ratings. In the first stage, a TOPSIS-based similarity index is calculated for each execution mode of the project activities, aggregating the additional objectives into a single comparable criterion derived from the weighted expert assessments. In the second stage, a four-objective optimization model is formulated to balance project makespan, cost, quality, and the similarity index. Integrating the similarity index ensures simultaneous consideration of both traditional and supplementary objectives. The multi-mode resource-constrained project scheduling problem (MRCPSP) is solved on small instances using the epsilon-constraint method, and for medium- and large-scale problems, two metaheuristics—NSGA-II and MOPSO—are employed. Computational results and sensitivity analyses conducted on the case study demonstrate the model’s capability to produce a diverse set of high-quality Pareto-optimal solutions within reasonable computational timeframes. The proposed framework’s flexibility makes it suitable for projects with varying objective priorities, offering a comprehensive and practical decision-support tool for project managers aiming for more balanced and precise scheduling decisions.

本文提出了一种新的两阶段方法来优化多目标建设项目的调度，并通过食品包装生产线的实际案例研究进行了验证。除了时间、成本和质量的传统目标之外，还有五个额外的因素——风险、范围蔓延、环境影响、利益相关者满意度和安全性——基于专家判断和标准化的绩效评级，被定量地纳入其中。在第一阶段，为项目活动的每一种执行模式计算基于topsis的相似性指数，将额外的目标汇总到从加权专家评估得出的单一可比标准中。在第二阶段，建立了一个四目标优化模型，以平衡项目完工时间、成本、质量和相似度指数。整合相似度指数可确保同时考虑传统目标和补充目标。采用epsilon约束方法求解小实例上的多模式资源约束项目调度问题，采用nsga - ii和mopso两种元启发式方法求解中大规模问题。对案例研究进行的计算结果和敏感性分析表明，该模型能够在合理的计算时间框架内产生各种高质量的帕累托最优解。所建议的框架的灵活性使其适合具有不同目标优先级的项目，为项目经理提供全面和实用的决策支持工具，以实现更平衡和精确的调度决策。

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引用次数: 0

Optimal control for resource allocation in a multi-patch epidemic model with gravity model-based human dispersal behavior 基于重力模型的人类扩散行为多斑块流行病模型中资源分配的最优控制

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-16 DOI: 10.1016/j.rico.2025.100648

A.S.K. Dinasiri , A.U.S. Adikari , H.C.Y. Jayathunga , I.T.S. Piyatilake

Mathematical models translate real-world problems into a structured framework, which makes it easier to investigate and analyze. Multi-patch compartmental models are used to model real-world scenarios related to epidemiology. Optimal control theory is used in this model to identify cost-effective strategies to minimize the proportion of individuals infected with COVID-19 in Sri Lanka. Since a nine-patch SIR-type model is considered in this research, human dispersal behaviors play a vital role. However, due to the lack of mobility data in Sri Lanka, a gravity model approach with a modified gravity model which models the human dispersal behaviors within and between patches is used to incorporate the human dispersal behaviors into the nine-patch SIR-type model. Then, the country is divided into three clusters using K-means clustering, based on the peak number of infections in each province without any control measures, for better representation. When using the control measure effective reproduction number

(R_{t})

represents the spread of the disease with sensitivity with the current susceptible population. It is observed that, in the absence of controls,

R_{t}

decreases from 1.55 to 1.30 within 400 days, and that it decreases from 1.57 to 0 within 20 days in the presence of controls. Control measures such as health measures and vaccination can control the disease within 40, 30–40, and 20–30 days in high-risk, moderate, and low-risk regions, respectively. Furthermore, results suggest that vaccination is the most efficient control strategy since it minimizes disturbing the lives of the general community rather than public health measures.

数学模型将现实世界的问题转化为一个结构化的框架，这使得它更容易调查和分析。多斑块区隔模型用于模拟与流行病学相关的现实世界情景。在该模型中使用最优控制理论来确定成本效益策略，以最大限度地减少斯里兰卡感染COVID-19的个体比例。由于本研究考虑的是一个9块sir型模型，因此人类的扩散行为起着至关重要的作用。然而，由于斯里兰卡缺乏流动性数据，我们采用了一个修正的重力模型方法来模拟人类在斑块内和斑块间的扩散行为，将人类的扩散行为纳入到9个斑块的sir型模型中。然后，根据没有任何控制措施的每个省的感染高峰数量，使用K-means聚类将该国分为三个聚类，以便更好地代表。当采用控制措施时，有效繁殖数（Rt）代表疾病在当前易感人群中的传播具有敏感性。可以观察到，在没有对照的情况下，Rt在400天内从1.55下降到1.30，在有对照的情况下，它在20天内从1.57下降到0。在高风险、中等和低风险地区，卫生措施和疫苗接种等控制措施可分别在40天、30-40天和20-30天内控制疾病。此外，结果表明，疫苗接种是最有效的控制策略，因为它最大限度地减少了对一般社区生活的干扰，而不是公共卫生措施。

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引用次数: 0

Improving diagnostic accuracy for PCOS: A hybrid machine learning architecture with feature selection, data balancing, and explainable AI techniques 提高PCOS的诊断准确性：一种混合机器学习架构，具有特征选择，数据平衡和可解释的AI技术

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-15 DOI: 10.1016/j.rico.2025.100647

Khandaker Mohammad Mohi Uddin , Abir Chowdhury , Md. Mahbubur Rahman Druvo , Mehreen Tabassum Jaima , Md. Tofael Ahmed Bhuiyan , Md. Manowarul Islam

Polycystic Ovary Syndrome (PCOS), which affects 5–10 % of women worldwide who are of reproductive age, is often misdiagnosed (∼70 %) despite the rising risks of metabolic disorders and infertility. Current machine learning diagnostics frequently struggle with unbalanced data and are not interpretable. This research improves PCOS diagnosis by introducing a new, interpretable hybrid architecture. We used mutual information and extra trees to improve feature selection and extensive preprocessing, including SMOTE for class imbalance, on a dataset of 541 patient records. A Soft Voting Ensemble that included Multilayer Perceptron (MLP) with CatBoost, optimized using GridSearchCV, and verified with 5-fold cross-validation, outperformed each individual model with previous research with a state-of-the-art accuracy of 96.88 %. Additionally, deep learning models performed well, most notably DANet (94.50 % accuracy). Importantly, SHAP and LIME improved model interpretability, offering clear insights into diagnostic judgments. The architecture was put into practice in an intuitive Flask web application for explainable, real-time forecasts. This study offers a therapeutically applicable method that strikes a balance between interpretability and high accuracy, enabling early PCOS identification and better patient outcomes. Multimodal integration and dataset extension are potential avenues for future study.

多囊卵巢综合征（PCOS）影响全世界5 - 10%的育龄妇女，尽管代谢紊乱和不孕症的风险不断上升，但仍经常被误诊（约70%）。当前的机器学习诊断经常与不平衡的数据作斗争，并且不可解释。本研究通过引入一种新的、可解释的混合架构来改善PCOS的诊断。我们使用互信息和额外的树来改进特征选择和广泛的预处理，包括针对类别不平衡的SMOTE，在541个患者记录的数据集上。包含多层感知器（MLP）和CatBoost的软投票集成，使用GridSearchCV进行优化，并经过5倍交叉验证，以96.88%的最先进精度优于先前研究的每个单独模型。此外，深度学习模型表现良好，最显著的是DANet（准确率为94.50%）。重要的是，SHAP和LIME提高了模型的可解释性，为诊断判断提供了清晰的见解。该架构在一个直观的Flask web应用程序中付诸实践，用于可解释的实时预测。本研究提供了一种治疗上适用的方法，在可解释性和高准确性之间取得平衡，使PCOS的早期识别和更好的患者预后成为可能。多模态集成和数据集扩展是未来研究的潜在途径。

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引用次数: 0