Purpose: Our research aims to compare the predictive performance of decision tree algorithms (DT) and logistic regression analysis (LR) in constructing models, and develop a Post-Thrombotic Syndrome (PTS) risk stratif...
详细信息
Purpose: Our research aims to compare the predictive performance of decision tree algorithms (DT) and logistic regression analysis (LR) in constructing models, and develop a Post-Thrombotic Syndrome (PTS) risk stratification tool. Methods: We retrospectively collected and analyzed relevant case information of 618 patients diagnosed with DVT from January 2012 to December 2021 in three different tertiary hospitals in Jiangxi Province as the modeling group. Additionally, we used the case information of 212 patients diagnosed with DVT from January 2022 to January 2023 in two tertiary hospitals in Hubei Province and Guangdong Province as the validation group. We extracted electronic medical record information including general patient data, medical history, laboratory test indicators, and treatment data for analysis. We established DT and LR models and compared their predictive performance using receiver operating characteristic (ROC) curves and confusion matrices. Internal and external validations were conducted. Additionally, we utilized LR to generate nomogram charts, calibration curves, and decision curves analysis (DCA) to assess its predictive accuracy. Results: Both DT and LR models indicate that Year, Residence, Cancer, Varicose Vein Operation History, DM, and Chronic VTE are risk factors for PTS occurrence. In internal validation, DT outperforms LR (0.962 vs 0.925, z = 3.379, P < 0.001). However, in external validation, there is no significant difference in the area under the ROC curve between the two models (0.963 vs 0.949, z = 0.412, P = 0.680). The validation results of calibration curves and DCA demonstrate that LR exhibits good predictive accuracy and clinical effectiveness. A web-based calculator software of nomogram (https://***/dynnomapp/) was utilized to visualize the logistic regression model. Conclusions: The combination of decisiontree and logistic regression models, along with the web-based calculator software of nomogram, can
With the continuous advancement of technology and the rapid development of automation technology, pharmaceutical companies urgently need to optimize and upgrade their financial operations with higher efficiency. To op...
详细信息
With the continuous advancement of technology and the rapid development of automation technology, pharmaceutical companies urgently need to optimize and upgrade their financial operations with higher efficiency. To optimize the business services of the financial center of pharmaceutical enterprises, the machine learning algorithm and robotic process automation technology are used to design suitable optimization methods. Based on the analysis of the departmental structure and business generation of the financial center of pharmaceutical enterprises, the optimization focus of business requirements is determined. Next, the decision tree algorithm is introduced to visually demonstrate the classification and decision-making process, optimizing the classification operation. Combined with robotic process automation technology, highly repetitive tasks are simplified to optimize business. The test results showed that the decision tree algorithm accurately classified different types of samples into the correct category when distinguishing them. The recall rate on reasonable financing schemes reached 0.69. The business processing time of robotic process automation technology was reduced by 50%, and the task completion rate was increased by 27%. The results indicate that the research designed business optimization method for pharmaceutical enterprise financial centers based on robot process automation technology significantly improves business processing efficiency, effectively controls costs, and enhances operational flexibility. In addition, the research provides theoretical support and practical guidance for the construction of intelligent financial center systems, promotes the automation and intelligent transformation of financial management, and makes important contributions to the development of this field.
These days, physical health concerns are gradually elevated to the same degree of significance as essential issues such as education, health, and protection as national living standards improve. Enhancing the physical...
详细信息
These days, physical health concerns are gradually elevated to the same degree of significance as essential issues such as education, health, and protection as national living standards improve. Enhancing the physical health of university students and encouraging healthy growth are significant moments for China, due to which each university controls a physical examination every year. Utilizing wireless sensors for data capture in university students physical fitness evaluation systems has played an essential role in various areas. Therefore, this paper presents a body domain network multimodal sensor and decisionalgorithm-based physical health assessment system for university students. The proposed system uses multiple body domain network sensors to collect physiological indicators data by using decisionalgorithms to combine multiple assessment indicators for analysis and prediction, ultimately generating a health assessment report and providing customized health advice and health plans for each user. In addition, to improve the physical health evaluation of university students in the context of sports medicine integration, this paper also proposes a physical health evaluation method based on the ID3 decision tree algorithm. It constructs a physical health information mining and feature extraction model for university students in the context of sports medicine integration. It uses a decisiontree optimization method for classification detection. The proposed method also uses an associative multidimensional feature detection algorithm to evaluate university students' physical fitness and health status. It establishes a decision indicator function to investigate information fusion and constrained feature decomposition through the significant difference and balanced training fusion methods. In the ID3 decisiontree, the branching system of college students' physical fitness health information in the context of sports medicine fusion is designed. It examines the entro
When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used...
详细信息
When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decisiontree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.
To mitigate haze impacts, three visibility simulation schemes were designed using decisiontree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and gr...
详细信息
To mitigate haze impacts, three visibility simulation schemes were designed using decisiontree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.
Purpose Unsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the pre...
详细信息
Purpose Unsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the present study proposes a framework for predicting safety performance before the implementation of projects. Design/methodology/approach The machine learning approach was adopted in this work. The proposed framework consists of two major phases: (1) data collection and (2) model development. The first phase involved several steps, including the identification of safety performance criteria, using a questionnaire to collect data, and converting the data into useful information. The second phase, on the other hand, included the use of the decision tree algorithm coupled with the k-Nearest Neighbors algorithm as the predictive tool along with the proposing modification strategies. Findings A total of nine safety performance criteria were identified. The results showed that safety employees, training, rule adherence and management commitment were key criteria for safety performance prediction. It was also found that the decision tree algorithm is capable of predicting safety performance. Originality/value The main novelty of the present study is developing an integrated model to propose strategies for the safety enhancement of projects in the case of incorrect predictions.
Simultaneous sensing of metabolic analytes such as pH and O-2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same ...
详细信息
Simultaneous sensing of metabolic analytes such as pH and O-2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O-2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O-2 and pH with a mean absolute error of < 4.50 center dot 10(-2) and < 1.96 center dot 10(-1), respectively, and a root mean square error of < 2.12 center dot 10(-1) and < 4.42 center dot 10(-1), respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.
Multi discs rotors are widely used in the industry. Shaft unbalance in multi-discs' rotors is the main failure origin that leads to global failures in rotary systems. Unbalance parameters that must be detected in ...
详细信息
Multi discs rotors are widely used in the industry. Shaft unbalance in multi-discs' rotors is the main failure origin that leads to global failures in rotary systems. Unbalance parameters that must be detected in the shaft are focused on this study. Unbalance parameters are eccentric mass value, eccentric radius, and disc number which are presenting an unbalance location. The main aim of the current paper is to identify unbalance parameters of a rotating shaft having multi-discs by artificial intelligent methods namely KNN and decision tree algorithm. For both algorithms, data derived from a fabricated test rig consists of a shaft in which four discs are mounted on that. The results show that the KNN presents more accuracy in estimating of unbalance parameters compared to the decisiontree in terms of unbalance locating. (C) 2019 Elsevier Ltd. All rights reserved.
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously...
详细信息
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being-in some cases-the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users' ages and the duration of the SM usage (***) were also considered. The decisiontree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.
With the growing popularity of the information science, more application is being integrated with websites that can be accessed directly through the internet. This has increased the possibility of attack by ill-legal ...
详细信息
With the growing popularity of the information science, more application is being integrated with websites that can be accessed directly through the internet. This has increased the possibility of attack by ill-legal persons to steal personal information. To identify a phishing assault, several strategies have been presented. However, there is still opportunity for progress in the fight against phishing. The objective of this research paper is to develop a more accurate prediction model using decisiontree (DT), Random Forest (RF) and Gradient Boosting Classifiers (GBC) with three features selection techniques Extra tree (ET), Chi-Square and Recursive Feature Elimination (RFE). Since phishing websites dataset contains 89 features, therefore we have applied extra tree and chi-square, feature selection method to identify the limited important features and then recursive features elimination technique has been used to reduce the dataset up-to optimum important features. We have compared the performance of the developed model using machine learning algorithms and find the best prediction performance using GBC, followed by RF and DT. These algorithmic models capture the trends from various cases of phishing with over R-square, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), in each case.
暂无评论