Optimal Model Selection Research Articles

Probabilistic code-based shear capacity model for I-shaped steel reinforced concrete (SRC) beams

A code-based probabilistic prediction model is developed for structural design and reliability analysis of the shear capacity of shear-critical steel reinforced concrete (SRC) beams. First, an experimental database of 83 shear tests was compiled from the literature, including steel-reinforced normal-strength concrete, steel-reinforced high-strength concrete, and steel reinforced lightweight aggregate concrete. Then, three deterministic formulations from the most commonly-used specifications JGJ, AIJ, and ACI-AISC were presented and compared against the database. Based on the different shear mechanism and formula considerations in the code-based deterministic models, this study developed 12 probabilistic models that account for both epistemic and aleatory uncertainty to examine the impact of concrete strength, shear span ratio, number of parameters, and their interactions. The Bayesian theory and Markov Chain Monte Carlo (MCMC) simulation method were utilized to calibrate the models by generating posterior distributions of the model parameters and the standard deviation (SD) of the model error. Optimal model selection hinges upon balancing precision and intricacy. An analysis was then conducted to evaluate the accuracy of the selected model, and the probabilistic model was applied to estimate the shear reliability of a SRC beam. Results demonstrate that the model objectively represents the probabilistic properties of the shear capacity of SRC beams and has the capability to conduct reliability analysis.

Hybrid Multi-Criteria Decision-Making Model for Optimal Selection of Cold Chain Logistics Service Providers

Hybrid Multi-Criteria Decision-Making Model for Optimal Selection of Cold Chain Logistics Service Providers

A novel randomized weighted fuzzy AHP by using modified normalization with the TOPSIS for optimal stock portfolio selection model integrated with an effective sensitive analysis

Building an ideal stock portfolio is a difficult and important undertaking in the world of financial decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique and the Randomised Weighted Fuzzy Analytic Hierarchy Process (AHP) are combined in this research to create a powerful model for selecting the best stocks for a portfolio. To improve the portfolio's robustness under various market scenarios, our model also incorporates sensitivity analysis. Given the inherent uncertainty in financial markets, the suggested approach starts by using modified fuzzy AHP based on introducing the randomized weights and the novel normalization to get expert views and assign suitable weights to the underlying selection criteria. Based on the established criteria and associated fuzzy scores, the TOPSIS approach is then used to rank and choose the best stocks. Our model also includes sensitivity analysis to evaluate the performance of the portfolio under various market conditions. Extensive empirical tests are performed utilizing historical stock market data to assess the effectiveness of our strategy. The outcomes show the suggested model's superiority in building stock portfolios that beat conventional approaches in terms of risk-adjusted returns.

Driver–passenger guidance model for Chinese freeway service areas based on path selection and parking delay

The driver–passenger guidance model proposed in this article is designed for large-scale freeway service areas that integrate various services such as shopping, leisure and catering. The model focuses on analyzing the impact of perceived travel distance and parking delay on path selection and parking space selection to optimize passenger flow efficiency. A perceived travel distance function is used to construct a pedestrian path selection model whose parameters are calibrated by a stated preference survey. An optimal parking space selection model is established based on the analytic hierarchy process-technique for order of preference based on similarity to the ideal solution method and considers parking distance, outdoor walking distance, indoor perceived travel distance after parking, and the impact of pedestrian crossings on parking delays. The model also calculates the vehicle arrival rate with parking time and the critical gap to ensure pedestrian safety. A parking delay calculation model is established based on the acceptable gap theory. The model is verified using the Yihezhuang service area of the Xiongan freeway as an example. The results show that the pedestrian path selection model improves passenger flow balance and reduces the variance in road passenger flow by 12% and per capita travel time by 9%. The proposed parking delay calculation model reduces parking delays by 30 s compared to models that do not consider pedestrian crossings, indicating its smoothness and convenience.

Isovariation model of the optimal choice of convergent decision scenarios of industrial enterprises taking into account compliance risks

Methods. In the process of improving the method of forming an isovariation model of optimal selection of convergent scenarios of investment decisions of industrial enterprises, taking into account compliance risks, the following methods were used: methods of analysis and synthesis, empirical methods, induction and deduction techniques, graphic and logical methods, methods of financial, economic and statistical analysis. The methodological basis of the research is the isovariation and scenario methods. Results. The article improves the method of forming an isovariation model for the optimal selection of convergent investment decision scenarios of industrial enterprises, taking into account compliance risks, which takes into account the individual characteristics of the development of an industrial enterprise and makes it possible to divide enterprises into groups depending on the best-case and worst-case scenario of influence according to the level of the criterion for evaluating the effectiveness of investments, the maximization of which indicates an effectively formed development strategy. In this simulation, software solutions act as a starting point for choosing and making scenario decisions. Isovariation models for the selection of convergent investment decision scenarios of industrial enterprises, taking into account compliance risks, consist of several investment project scenarios, characterized by a different set of indicators of expected income and risk. Models are considered effective if the value of expected income leads to the minimization of risk or the highest possible indicator is associated with a certain level of risk. Novelty consists in improving the method of forming an isovariation model for choosing convergent investment decision scenarios of industrial enterprises, taking into account compliance risks. Practical value. Using the isovariation model for choosing convergent investment decision scenarios of industrial enterprises taking into account compliance risks it is possible to estimate the value of mathematical expectations of enterprise incomes. The moment of uncertainty is associated with an attempt to evaluate scenarios, which is a parametric isovariational component of positive and negative deviations. Thus, for each convergent scenario, the mathematical expectation of income and degree of risk can be determined. Among several effective scenarios, you can choose the optimal one depending on the investor's attitude to risk.

975. Age and Body Mass Index Impact Optimal Vancomycin Model Selection for Bayesian Dosing: Results from a Large Multi-site Retrospective Study of Adult Patients

Abstract Background Vancomycin population pharmacokinetic (popPK) models embedded in Bayesian software are crucial to informing optimal initial dosing. Since pharmacokinetics (PK) vary based on patients’ age and body mass index (BMI), pharmacists are faced to select the best model amongst many for their patients. To aid in model selection, we used a large multi-site database to determine which models were the most accurate across age and BMI categories. Methods Data on adult patients receiving intravenous vancomycin therapy during January 1, 2022 - December 31, 2022, at healthcare organizations across the U.S. were queried from a Bayesian software database. Data collected included age, weight, height, serum creatinine, sex, vancomycin doses received, and vancomycin concentration levels. The a priori root mean square error (RMSE) for six well-performing and previously validated models was calculated for each patient to measure what the accuracy would have theoretically been if each were used. The average RMSE value per model was calculated and compared between age and BMI categories shown in Table 1. Each age-BMI combination’s most accurate model was reported. Results There were 79,600 patient treatment courses included across 84 health systems. The results for the most accurate model for each age-BMI combination are reported in Table 2. The lowest RMSE was 3.91 mg/L using the modified Goti model in patients > 80 years with BMI < 18.5 kg/m2. The highest RMSE was 7.86 mg/L using the Carreno model in patients < 35 years with BMI > 30 and < 35 kg/m2. A relationship was observed where older age categories lead to better model performance. Conclusion Age and BMI categories influenced which vancomycin popPK models performed best a priori within a Bayesian software when studied within a very robust dataset. These findings may help pharmacists choose which model to use for the most accurate PK calculations and thus inform optimal initial pop-PK vancomycin dosing. It also highlights that adults under 35 years, which tend to be underrepresented in model development populations, showed higher prediction error. Further research is needed on how to best implement these findings into practice. Disclosures Maria-Stephanie Hughes, PharmD, InsightRX: Stocks/Bonds Tiffany Lee, PharmD, InsightRX: Employment

Mathematical model of optimal selection of information security measures in the design of an integrated security system at an informatization facility

Mathematical model of optimal selection of information security measures in the design of an integrated security system at an informatization facility

Optimal siting of substations of traction power supply systems considering seismic risk

The traction power supply system (TPSS) is a critical infrastructure that provides electrified railways with power. However, its safe and reliable operation is threatened by earthquake disasters. Due to the strong randomness, uneven spatial distribution, and destructive risk of earthquake disasters, it is crucial to develop reasonable planning schemes to mitigate seismic risks of the system. This paper proposes a substation optimal siting method considering seismic risks. By establishing the chain circuit model of the TPSS and employing the dynamic power flow calculation method, the power loss of the system is obtained. By integrating the earthquake scenario simulation and substation fragility analysis, the substation seismic risk is calculated through Monte Carlo simulation. On this basis, the optimization model for substation site selection is formulated, aiming to minimize both the system power loss and the substation seismic risk while adhering to constraints of construction cost, operating safety, and geographical locations. The Pareto optimal solutions are then obtained. Simulation analysis based on actual system parameters shows that compared with traditional site selection methods, the proposed method can effectively mitigate system seismic risks while considering system power loss, providing theoretical bases for the disaster prevention and mitigation in the TPSS planning.

A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection

In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.

Utilizing bee foraging behavior in mutational salp swarm for feature selection: a study on return-intentions of overseas Chinese after COVID-19

Abstract We present a bee foraging behavior-driven mutational salp swarm algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. The improved bee foraging strategy is leveraged in the follower location update phase to break the fixed range search of salp swarm algorithm, while the unscented mutation strategy on the optimal solution is employed to enhance the quality of the optimal solution. Extensive experimental results on public CEC 2014 benchmark functions validate that the proposed BMSSA performs better than nine well-known metaheuristic methods and seven state-of-the-art algorithms. The binary BMSSA (bBMSSA) algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on 12 UCI datasets demonstrate the superiority of bBMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after coronavirus disease (COVID-19) through an anonymous online questionnaire and performed a case study by setting up a bBMSSA-based feature selection optimization model. The outcomes manifest that the bBMSSA-based feature selection model exhibits a conspicuous prowess, attaining an accuracy exceeding 93%. The case study shows that the development prospects, the family and job in the place of residence, seeking opportunities in China, and the possible time to return to China are the critical factors influencing the willingness to return to China after COVID-19.

A novel deep learning based intrusion detection system for the IoT-Cloud platform with blockchain and data encryption mechanisms

The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology’s rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user’s privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2CC) mechanism. Finally, in the data retrieval phase, the user’s authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models.

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.

Enhancing Parkinson's disease severity assessment through voice-based wavelet scattering, optimized model selection, and weighted majority voting

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact an individual's quality of life. Voice changes have shown promise as early indicators of PD, making voice analysis a valuable tool for early detection and intervention. This study aims to assess and detect the severity of PD through voice analysis using the mobile device voice recordings dataset. The dataset consisted of recordings from PD patients at different stages of the disease and healthy control subjects. A novel approach was employed, incorporating a voice activity detection algorithm for speech segmentation and the wavelet scattering transform for feature extraction. A Bayesian optimization technique is used to fine-tune the hyperparameters of seven commonly used classifiers and optimize the performance of machine learning classifiers for PD severity detection. AdaBoost and K-nearest neighbor consistently demonstrated superior performance across various evaluation metrics among the classifiers. Furthermore, a weighted majority voting (WMV) technique is implemented, leveraging the predictions of multiple models to achieve a near-perfect accuracy of 98.62%, improving classification accuracy. The results highlight the promising potential of voice analysis in PD diagnosis and monitoring. Integrating advanced signal processing techniques and machine learning models provides reliable and accessible tools for PD assessment, facilitating early intervention and improving patient outcomes. This study contributes to the field by demonstrating the effectiveness of the proposed methodology and the significant role of WMV in enhancing classification accuracy for PD severity detection.

Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

Optimization model for multi-products multi-periods multi-suppliers raw-material selection and composition, and order quantity problem with one-year minimum order quantity contract

This paper concerns the optimization model for a multi-product multi-period raw-material selection and composition, and order quantity problem faced by a beverage company.•There are some criteria in raw material selection, which we accommodate all the criteria in the objective function. There are several suppliers, and one of the decision criteria is a one-year minimum order quantity contract between the company and the suppliers. The actual one-year demand for raw materials may deviate significantly from the one-year minimum order quantities.•We derive a function that can be regarded as a penalty function to maintain the total order quantities in one year to fulfill the minimum one-year order quantity contracts. This penalty function is a part of the objective function and can be relaxed once the one-year minimum order quantity contracts are fulfilled.•We performed several numerical experiments to check the optimal solutions for various demands and for various objective functions. These experiments show our MILP (Mixed Integer Linear Programming) gives the desired optimal solutions and show the influence of decision criteria on the optimal solution.

The Optimal Selection of Ideological and Political Issues in Business Courses Based on Swarm Intelligence Algorithm

The current optimal selection matrix for ideological and political issues of business courses is mostly set as a single objective form, and the topic selection is limited in scope, increasing the mutation rate of the optimal selection of topics. Therefore, the design and analysis of the optimal selection method for ideological and political issues of business courses based on the swarm intelligence algorithm is proposed. According to the actual measurement needs and standards, extract the optimal characteristic of ideological and political issues selection of the curriculum, use a multi-level approach to break the limits of issues selection, establish a multi-level swarm intelligence selection matrix, build an optimal selection model for ideological and political issues of business and trade courses based on swarm intelligence accounting, and achieve the optimal selection of issues through group fixed-point optimization. The novelty of this work lies in the design and analysis of the optimal selection method for ideological and political issues in business courses using a swarm intelligence algorithm. This approach introduces a new way of selecting topics by harnessing the power of collective intelligence inspired by the behavior of insects or animals. The final test results show that the mutation rate of the optimal selection of the three topics finally screened using the swarm intelligence algorithm is better controlled below 0.2 through the measurement of five classes, indicating that the topic is more practical, more targeted, and has better discussion value.

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction.

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

Cross-Layer Based QoS Aware Load-balancing Multi-Path Routing Protocol over Wireless Multimedia Sensor Networks

Abstract: Currently, the utilization of WMSNs in different real-time and non-real-time applications requires an excessive amount of bandwidth for reliable data delivery. The unique features of WMSNs are significantly challenging in satisfying the QoS requirements in such application-specific environments and balancing the traffic load among the devices. The provision of reliable multipath routing is a cornerstone in fulfilling the QoS requirements of WMSNs. Selecting multiple optimal paths between a source and destination based on peculiar routing metrics enhances the performance of QoS routing. Generally, routing protocols exploit several routing metrics, such as delay, remaining energy of nodes, hop count, available bandwidth, and packet loss rate in path selection to attain high reliability in data delivery. Many existing routing protocols only consider the network layer parameters, whereas it lacks focus on the data link and physical layer parameters, which creates a severe impact on the degradation of QoS. In addition to that, varying bandwidth channels create interference in multimedia data delivery and degrade the network performance. Designing a multipath routing protocol by considering cross-layer parameters offer a promising solution to optimize the WMSN performance. In cross-layer design, diverse protocol layers support the routing decisions adaptively by perceiving the dynamic characteristics of the wireless medium, resulting in fair use of scarce resources with high QoS. A Cross-Layer Based QoS Aware Load-Balancing Multipath Routing Protocol over Wireless Multimedia Sensor Networks was the goal of the study's five design objectives. The study and analysis of QoS and cross-layer-based routing algorithms for WMSNs was the initial goal. Secondly, a Deep Learning prioritization-based packet classifier to divide traffic according to priority. To ensure fair resource consumption and distribution of multimedia traffic, the third goal was to design and create a cross-layer optimizer model for optimal multiple disjoint route selection using machine learning techniques. The development of a cutting-edge channel-scheduling algorithm was goal four. It was designed to efficiently assign low-interference channels to communication devices in order to lower the packet drop rate in real-time packet delivery. Last but not least, a security method for Wireless Multimedia Sensor Networks' Cross-Layer based multipath routing protocol.

How do carbon trading price and carbon tax rate affect power project portfolio investment and carbon emission: An analysis based on uncertainty theory

Responding to the social, economic, and environmental call to resolve current sustainability challenges, the concern about carbon dioxide emission reduction should be incorporated into the power investment decision process. Reflecting the low carbon emission requirement, this paper proposes a new optimization model for power project portfolio selection that simultaneously considers both of carbon emission trading scheme and carbon tax imposition. In this model, the initial investment outlay, the power sale price, the carbon trading price, and carbon tax rate are treated as uncertain variables considering the fast-changing environment and complex market situation. Incorporating the constraint on whether the carbon quota is exceeded into the model, two investment strategies are proposed for investors. Using the proposed model, the impact of the rises in carbon trading price and carbon tax rate on the investor’s investment strategy selection and the carbon emission is simulated and analyzed through a case study. When the expected carbon price is 203.50 RMB/tCO2-eq or less, a company should invest based on the strategy that annual emissions exceed the quota to obtain a maximum expected NPV which is larger than 408588 million RMB. When future carbon prices are 352.00, 500.50 and 649.00 RMB/tCO2-eq, the government should impose carbon tax rates of 30, 30, and 40 RMB/tCO2 on a power company, respectively, to obtain carbon emission reduction effect. At last, to see the contrast effect of the results from simultaneous implementation of both carbon trading and carbon tax, the results considering the carbon trading alone or carbon tax alone are discussed, respectively.

Diabetes risk prediction model based on community follow-up data using machine learning

Diabetes is a chronic metabolic disease characterized by hyperglycemia, the follow-up management of diabetes patients is mostly in the community, but the relationship between key lifestyle indicators in community follow-up and the risk of diabetes is unclear. In order to explore the association between key life characteristic indicators of community follow-up and the risk of diabetes, 252,176 follow-up records of people with diabetes patients from 2016 to 2023 were obtained from Haizhu District, Guangzhou. According to the follow-up data, the key life characteristic indicators that affect diabetes are determined, and the optimal feature subset is obtained through feature selection technology to accurately assess the risk of diabetes. A diabetes risk assessment model based on a random forest classifier was designed, which used optimal feature parameter selection and algorithm model comparison, with an accuracy of 91.24% and an AUC corresponding to the ROC curve of 97%. In order to improve the applicability of the model in clinical and real life, a diabetes risk score card was designed and tested using the original data, the accuracy was 95.15%, and the model reliability was high. The diabetes risk prediction model based on community follow-up big data mining can be used for large-scale risk screening and early warning by community doctors based on patient follow-up data, further promoting diabetes prevention and control strategies, and can also be used for wearable devices or intelligent biosensors for individual patient self examination, in order to improve lifestyle and reduce risk factor levels.