Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.

A clinical decision support system using rough set theory and machine learning for disease prediction

Objective Technological advances have led to drastic changes in daily life,and particularly healthcare,while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient health-care records with digital files.Using the latest technology and data mining techniques,we aimed to develop an automated clinical decision support system(CDSS),to improve patient prognoses and healthcare delivery.Our proposed approach placed a strong emphasis on improvements that meet patient,parent,and physician expec-tations.We developed a flexible framework to identify hepatitis,dermatological conditions,hepatic disease,and autism in adults and provide results to patients as recommendations.The novelty of this CDSS lies in its inte-gration of rough set theory(RST)and machine learning(ML)techniques to improve clinical decision-making accuracy and effectiveness.Methods Data were collected through various web-based resources.Standard preprocessing techniques were applied to encode categorical features,conduct min-max scaling,and remove null and duplicate entries.The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values,respectively.A rough set approach was applied as feature selection,to remove highly redundant and irrelevant elements.Then,various ML techniques,including K nearest neighbors(KNN),linear support vector machine(LSVM),radial basis function support vector machine(RBF SVM),decision tree(DT),random forest(RF),and Naive Bayes(NB),were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle.The model was implemented in Python,and various validity metrics,including precision,recall,F1-score,and root mean square error(RMSE),applied to measure its performance.Results Features were selected using an RST approach and examined by RF analysis and important features of hepatitis,dermatology conditions,hepatic disease,and autism determined by RST and RF exhibited 92.85%,90.90%,100%,and 80%similarity,respectively.Selected features were stored as electronic health records and various ML classifiers,such as KNN,LSVM,RBF SVM,DT,RF,and NB,applied to classify patients with hepatitis,dermatology conditions,hepatic disease,and autism.In the last phase,the performance of proposed classifiers was compared with that of existing state-of-the-art methods,using various validity measures.RF was found to be the best approach for adult screening of:hepatitis with accuracy 88.66%,precision 74.46%,recall 75.17%,F1-score 74.81%,and RMSE value 0.244;dermatology conditions with accuracy 97.29%,precision 96.96%,recall 96.96%,F1-score 96.96%,and RMSE value,0.173;hepatic disease,with accuracy 91.58%,precision 81.76%,recall 81.82%,F1-Score 81.79%,and RMSE value 0.193;and autism,with accuracy 100%,precision 100%,recall 100%,F1-score 100%,and RMSE value 0.064.Conclusion The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis,dermatology conditions,hepatic disease,and autism.

Building up Multi－Layered Perceptrons as Classifier System for Decision Support

This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.

Decision tree support vector machine based on genetic algorithm for multi-class classification

To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.

A behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant,mid-gestation,and late-gestation sows

Background:Although thermal indices have been proposed for swine,none to our knowledge differentiate by reproductive stage or predict thermal comfort using behavioral and physiological data.The study objective was to develop a behavior and physiology-based decision support tool to predict thermal comfort and stress in multiparous(3.28±0.81)non-pregnant(n=11),mid-gestation(n=13),and late-gestation(n=12)sows.Results:Regression analyses were performed using PROC MIXED in SAS 9.4 to determine the optimal environmental indicator[dry bulb temperature(TDB)and dew point]of heat stress(HS)in non-pregnant,mid-gestation,and lategestation sows with respiration rate(RR)and body temperature(TB)successively used as the dependent variable in a cubic function.A linear relationship was observed for skin temperature(T_(S))indicating that TDB rather than the sow HS response impacted T_(S)and so T_(S)was excluded from further analyses.Reproductive stage was significant for all analyses(P<0.05).Heat stress thresholds for each reproductive stage were calculated using the inflections points of RR for mild HS and TB for moderate and severe HS.Mild HS inflection points differed for non-pregnant,mid-gestation,and late gestation sows and occurred at 25.5,25.1,and 24.0℃,respectively.Moderate HS inflection points differed for non-pregnant,mid-gestation,and late gestation sows and occurred at 28.1,27.8,and 25.5℃,respectively.Severe HS inflection points were similar for non-pregnant and mid-gestation sows(32.9℃)but differed for late-gestation sows(30.8℃).These data were integrated with previously collected behavioral thermal preference data to estimate the TDB that non-pregnant,mid-gestation,and late-gestation sows found to be cool(TDB<TDB preference range),comfortable(TDB=TDB preference range),and warm(TDB preference range<TDB<mild HS).Conclusions:The results of this study provide valuable information about thermal comfort and thermal stress thresholds in sows at three reproductive stages.The development of a behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant,mid-gestation,and late-gestation sows is expected to provide swine producers with a more accurate means of managing sow environments.

Methods of Classification of Ordnance Materials Military-Civilian Joint Support Categories Based on Multiple Attribute Decision

Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis.Among ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification tasks.The network weights and the activation functions are the two crucial elements in the learning process of an ANN.These weights affect the prediction ability and the convergence efficiency of the network.In traditional settings,ANNs assign random weights to the inputs.This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights.The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer classification.This system computes the confusion matrix-based metrics for traditional and proposed frameworks.The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other models.The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research limitations.Moreover,the proposed framework is of use to predict and classify cancer patients accurately.Consequently,this will facilitate the effective management of cancer patients.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

中医优势病种临床决策支持系统在“混合痔”中医病历质量控制中的应用

目的:运用中医优势病种临床决策支持系统,提升中医辨证分型的准确性,提升中医病历书写理法方药的一致性,提高中医病历书写质量,提高中医诊疗服务质量。方法:采集2023年3—5月运用中医优势病种临床决策支持系统的混合痔病例163例和2022年3—5月未运用中医优势病种临床决策支持系统的混合痔病例176例,回顾性调查分析两组的方药使用量、中医辨证分型准确性、中药饮片的用药记录、病历书写的理法方药一致性。结果:两组方药使用人次、人均开方次数、人均辨证次数、方药病历记录比较,差异均无统计学意义(P>0.05)。智慧医疗辨证论治组方药内容与病历记录一致性比例[95.45%(147/154)]高于传统医疗辨证论治组[90.36%(150/166)],但差异无统计学意义(P>0.05)。智慧医疗辨证论治组的病历记录中患者病象与疾病证型一致比例[96.75%(149/154)],证型、治则治法、方药三者一致比例[86.14%(143/166)],辨证证型变更比例[56.49%(87/154)]均高于传统医疗辨证论治组的86.14%(143/166)、77.77%(129/166)和7.22%(12/166),差异均有统计学意义(P<0.01或P<0.05)。结论:使用中医优势病种临床决策支持系统有助于规范中医辨证思路,培养中医思维模式,提升病历书写者理法方药的逻辑性,有利于提高中医诊疗服务质量。

基于GF-1卫星影像数据融合的冬小麦田空间信息提取

为给高标准农田建设规划和粮食安全生产措施的制定提供准确信息,在对国产GF-1/PMS卫星影像进行辐射定标、大气校正、几何校正和裁剪等预处理的基础上,经过影像融合提取了高标准麦田多地物的点像元光谱信息,通过分析不同地物光谱特征,利用波段反射率、归一化差值植被指数(NDVI)和差值植被指数(DVI)构建植被光谱特征指标阈值,进而对冬小麦田及非麦田进行分类,以获取高标准麦田的空间分布信息。结果表明,光谱特征指标选择BR_(4)>0.3、NDVI>0.619和DVI>0.317,可以较准确地从影像中识别出冬小麦田,并减少田间道路被误判为冬小麦田像元。在非麦田分类中,选择BR_(3)>0.15和BR_(4)>0.2,可将建筑用地与河流(沟渠)区分开。利用田间样方统计面积和遥感提取面积进行精度验证,总体精度可达97.33%,说明通过中、高空间分辨率遥感数据融合,结合多重光谱特征指标建立合理的分类阈值,可以准确提取冬小麦田及非麦田的分布信息。

基于Google Earth Engine的前郭县春季农田覆膜提取

本文基于Google Earth Engine(GEE)云平台,综合考虑光学影像的波段反射率、光谱指数特征和雷达影像的极化、纹理特征,分别构建仅使用光学特征、仅使用雷达特征以及光学和雷达特征组合3种特征输入组合;根据精度确定最佳输入特征后,分别结合机器学习中的分类与回归树、支持向量机、最小距离分类法、梯度提升树和随机森林5种方法建立覆膜提取模型,依据结果精度评估不同方法的性能,并基于最优化模型提取出最终的覆膜农田面积。结果表明:①最佳输入特征为波段反射率特征+光谱指数特征+极化特征+纹理特征;②采用随机森林方法建立的模型精度最高,研究区I的总体精度达到了95.84%,Kappa系数为0.95,地物错分率为1.2%,明显优于其他4种方法(地物错分率较分类与回归树、支持向量机、最小距离和梯度提升树法降低0.8%、7.3%、38.0%和0.3%),研究区II的验证精度达到了87.84%,证明该模型在覆膜提取中可以取得更加准确的结果;③使用本文方法得到2022年研究区I覆膜农田面积为1302.48 km2,估算地膜使用量约为7585.62 t。本文综合考虑光学和雷达影像在地物识别中的特点建立模型,可以准确、高效的识别农田地膜,掌握地膜面积,对环境治理与防治具有重要意义。

阶段性肠内营养健康教育对上消化道出血患者康复效果的研究

目的探讨阶段性肠内营养健康教育对上消化道出血患者康复效果的影响。方法选择2022年6月至2023年12月韶关第一人民医院收治的100例急性上消化道出血患者展开研究,按随机数表法分为观察组和对照组各50例。在常规治疗基础上,对照组患者给予肠内营养护理干预,观察组在对照组的基础上采用基于渥太华决策支持框架(ODSF)的阶段性肠内营养健康教育模式干预,两组患者均持续干预至出院。比较两组患者的弃去营养制剂率、下床活动时间、住院时间,以及干预前后的血清血红蛋白(Hb)、白蛋白(ALB)、前白蛋白(PA)水平,并比较两组患者的治疗依从性。结果干预后,观察组患者的弃去营养制剂率为6.00%,明显低于对照组的20.00%,下床活动时间、住院时间分别为(5.98±1.45)d、(8.24±1.65)d,明显短于对照组的(6.67±1.69)d、(9.57±1.72)d,差异均有统计学意义(P<0.05);干预后,观察组患者的血清Hb、ALB、PA水平分别为(123.47±10.53)g/L、(39.30±3.15)g/L、(219.34±25.07)mg/L,明显高于对照组的(110.35±8.37)g/L、(34.27±2.94)g/L、(205.33±21.18)mg/L,差异均有统计学意义(P<0.05);观察组患者的治疗总依从率为90.00%,明显高于对照组的72.00%,差异有统计学意义(P<0.05)。结论阶段性肠内营养健康教育有助于促进上消化道出血患者康复,值得临床推广应用。

Development of an Effective System for Selecting Construction Materials for Sustainable Residential Housing in Western Australia

Urbanization and living comfort have revolutionized the construction industry. Many techniques and strategies have been used to improve the overall efficiency of construction and to reduce waste during and after the construction activity;some are cost effective and some not. Sustainable construction strategies have addressed these issues by proposing relatively more cost effective and environment-saving solutions. One strategy is to select sustainable construction materials at the building design stage. This article involved a questionnaire survey to collect data about local technical stakeholders’ (architects, designers, engineers, estimators, and managers) awareness of environmental sustainability and current practices for selecting construction materials. A sustainability index (SI) was developed using SPSS (Statistical Package for the Social Sciences) for the complex statistical analysis. These data were used to develop a decision support system (DSS) using the multi-criteria decision making (MCDM) technique, the TOPSIS. The support system was validated by applying it to sustainable roof products in a pilot case study—these materials are frequently used in local markets for residential construction in West Australia. So the main objective was to get insight to local market trends and features involved in construction materials selection. Data analysis was carried out to develop a decision support system to help technical stakeholders in construction materials selection process.

基于机器学习的煤巷围岩稳定性预测与应用

煤巷围岩稳定性分类对指导现场岩体工程设计、施工、管理具有重要的理论和工程实际意义。本文选取了影响煤巷围岩稳定性的7个关键指标,采用现场案例、调查问卷和文献计量等方法收集样本并建立了围岩稳定性分类数据库,基于6种机器学习方法分别建立了煤巷围岩稳定性分类预测模型。经模型计算得出,神经网络和改进的支持向量机模型具有较高的预测准确性。将模型应用于霍州矿区实际工程,结果表明,神经网络和改进的支持向量机方法预测精度高、可靠性好。

决策树分类算法的预剪枝与优化

决策树分类算法是1种直观、有效的分类算法。针对影响决策树算法分类效果的2个重要因素———属性选择度量及预剪枝参数,对算法进行优化。以澳大利亚某地降水预测为实例,搭建迭代二叉树3代(ID3)及分类与回归树(CART)模型并对其进行优化。通过数据预处理及预剪枝操作,改进了算法,有效防止了过拟合,提高了决策树的分类性能。基于交叉检验方法优化了2种模型的参数,提高了预测精度。性能对比结果表明,基于基尼指数构建的决策树精度更高。针对该决策树,在优化后的参数附近构建三维网络搜索最优参数,达到了更高的预测准确率。

污染场地可持续风险管控区划研究进展

“十四五”时期我国土壤生态环境保护形势依然严峻,面对污染场地数量多、资金压力大、技术力量薄弱和城市发展用地需求不断扩大等诸多挑战,迫切需要揭示污染场地风险管控与区域可持续发展的交互作用机制,建立污染场地风险管控区划技术体系与分类管理决策支持系统。文章在充分调研国内外已有研究的基础上,从基于健康风险评价的特定污染场地分区管控、基于风险分级的区域污染场地分类管理和基于污染场地再利用的风险管控区划规划决策等3个方面分别阐述了污染场地可持续风险管控区划研究的基本思路,为统筹构建国家层面的区划技术体系提出了完善区划指标体系、兼顾利益相关方诉求和整合有效信息等可优化方向。通过研究进展整理与未来研究展望,以期为我国污染场地风险管控和再利用的分级、分类、分区管理提供精准决策支持,最终推动城市社会经济的可持续有序发展。

考虑颜色特征最优组合的CART决策树火灾图像识别方法

针对火灾图像识别过程中颜色特征数量多、特征间相关性复杂、难以在多维特征融合过程中有效融合图像颜色特征等问题,提出1种考虑颜色特征最优组合的CART决策树火灾图像识别方法。首先,在Lab、RGB、HSV 3种色彩模式下基于图像颜色特征提取火灾图像特征序列;其次,分别在3种色彩模式下基于精细决策树与特征随机排列组合方法提取颜色特征中最优组合特征;最后,将提取的火灾图像最优组合特征序列作为CART决策树输入进行模型训练,并通过测试样本以及其他机器学习方法进行模型泛化能力的分析。研究结果表明:本文方法寻找出识别火灾图像的最优颜色特征组合为“Kb1+Var1+Kg+Kb2+Var2+Kh+Ks+Kv”;CART决策树方法对于火灾图像识别的测试准确度可达84.5%,其分类效果明显优于其他决策树类与集成树类方法;9折为最佳交叉验证折数,其测试准确度可达86.47%,与5折交叉验证相比明显提升14.77%。研究结果可为火灾图像识别提供方法基础。