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Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models

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CANCER CELL INTERNATIONAL 2024年第1期24卷 1-4页

作者： Xu, Fangshi Li, Zongyu Guan, Hao Ma, Jiancang Xi An Jiao Tong Univ Affiliated Hosp 2 Dept Vasc Surg 157 West Five Rd Xian 710004 Shaanxi Peoples R China

Bioinformatics models greatly contribute to individualized assessments of cancer patients. However, considerable research neglected some critical technological points, including comparisons of multiple modeling algorithms, evaluating gain effects of constructed model, comprehensive bioinformatics analyses and validation of clinical cohort. These issues are worthy of emphasizing, which will better serve future cancer research.

关键词： machine learning algorithm Gain effect Bioinformatics Prognostic model Clinical validation

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Design and Python Simulation of Cross-border E-commerce Logistics Pathways Based on machine learning algorithms 24

Design and Python Simulation of Cross-border E-commerce Logi...

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1st International Conference on Image Processing machine learning and Pattern Recognition

作者： Liu, Jian Lei, Fei Liu, Qi Yuan, Huan Guangdong Univ Sci & Technol Sch Finance & Econ Dongguan Peoples R China Guangdong Univ Sci & Technol Sch Management Dongguan Peoples R China

ISBN: (纸本)9798400707032

In the era of globalization, cross-border e-commerce has emerged as a significant driver of international trade, necessitating efficient logistics pathways to enhance delivery speed and reduce costs. This study presents a comprehensive approach to designing and simulating cross-border e-commerce logistics pathways using machine learning algorithms. We first identify the key factors influencing logistics efficiency, including geographical, regulatory, and demand variability aspects. Utilizing a dataset comprising historical shipping data, we apply various machine learning techniques, such as decision trees, random forests, and neural networks, to model and predict optimal logistics routes. The proposed model integrates real-time data processing and predictive analytics to dynamically adapt to changing conditions and optimize the logistics pathways in real-time. A Python-based simulation framework is developed to visualize and test the effectiveness of the proposed logistics pathways under different scenarios. The results demonstrate significant improvements in delivery times and cost reductions when compared to traditional logistics strategies. This research not only contributes to the field of logistics and supply chain management but also provides practical insights for e-commerce businesses seeking to enhance their operational efficiency in the competitive cross-border market. Future work will focus on refining the model with additional variables and exploring the integration of blockchain technology to further enhance transparency and traceability in cross-border logistics operations.

关键词： machine learning algorithm Python Simulation Random Forest Cross-border e-commerce logistics path design

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Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease

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INTERNATIONAL JOURNAL OF CARDIOLOGY 2023年第1期383卷 175-184页

作者： Liu, Yang Xue, Junshuai Jiang, Jianjun Shandong Univ Qilu Hosp Dept Gen Surg Vasc Surg 107 Rd Wen Hua Xi Jinan Shandong Peoples R China Shandong Univ Qilu Hosp Dept Gen Surg 107 Rd Wen Hua Xi Jinan Shandong Peoples R China

Background: This study aimed to apply eight machine learning algorithms to develop the optimal model to predict amputation-free survival (AFS) after first revascularization in patients with peripheral artery disease (PAD).Methods: Among 2130 patients from 2011 to 2020, 1260 patients who underwent revascularization were randomly assigned to training set and validation set in an 8:2 ratio. 67 clinical parameters were analyzed by lasso regression analysis. Logistic regression, gradient boosting machine, random forest, decision tree, eXtreme gradient boosting, neural network, Cox regression, and random survival forest (RSF) were applied to develop prediction models. The optimal model was compared with GermanVasc score in testing set comprising patients from 2010. Results: The postoperative 1/3/5-year AFS were 90%, 79.4%, and 74.1%. Age (HR:1.035, 95%CI: 1.015-1.056), atrial fibrillation (HR:2.257, 95%CI: 1.193-4.271), cardiac ejection fraction (HR:0.064, 95%CI: 0.009-0.413), Rutherford grade >= 5 (HR:1.899, 95%CI: 1.296-2.782), creatinine (HR:1.03, 95%CI: 1.02-1.04), surgery duration (HR:1.03, 95%CI: 1.01-1.05), and fibrinogen (HR:1.292, 95%CI: 1.098-1.521) were independent risk factors. The optimal model was developed by RSF algorithm, with 1/3/5-year AUCs in training set of 0.866 (95% CI:0.819-0.912), 0.854 (95% CI:0.811-0.896), 0.844 (95% CI:0.793-0.894), in validation set of 0.741 (95% CI:0.580-0.902), 0.768 (95% CI:0.654-0.882), 0.836 (95% CI:0.719-0.953), and in testing set of 0.821 (95%CI: 0.711-0.931), 0.802 (95%CI: 0.684-0.919), 0.798 (95%CI: 0.657-0.939). The c-index of the model outperformed GermanVasc Score (0.788 vs 0.730). A dynamic nomogram was published on shinyapp (https://wyy ***/amputation/). Conclusion: The optimal prediction model for AFS after first revascularization in patients with PAD was developed by RSF algorithm, which exhibited outstanding prediction performance.

关键词： Peripheral artery disease Amputation-free survival machine learning algorithm Prediction model Electronic medical records Random survival forest

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Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2023年第23期41卷 14285-14298页

作者： Ma, Zhang-Wen Tang, Jia-Wei Liu, Qing-Hua Mou, Jing-Yi Qiao, Rui Du, Yan Wu, Chang-Yu Tang, Dao-Quan Wang, Liang Xuzhou Med Univ Sch Pharm Dept Pharmaceut Anal Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Jiangsu Key Lab New Drug Res & Clin Pharm Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Dept Intelligent Med Engn Xuzhou Jiangsu Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Taipa Macau Peoples R China Xuzhou Med Univ Sch Clin Med 1 Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Dept Clin Pharm Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Med Imaging Dept Biomed Engn Xuzhou Jiangsu Peoples R China Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Lab Med Guangzhou Guangdong Peoples R China

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

关键词： Mulberry leaf geographic origin Raman spectrometry machine learning algorithm convolutional neural network

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Estimating the compressive strength of plastic concrete samples using machine learning algorithms

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Asian Journal of Civil Engineering 2024年第2期25卷 1503-1516页

作者： Alishvandi, Ali Karimi, Javad Damari, Siavash Moayedi Far, Arsham Setodeh Pour, Mohammad Ahmadi, Morteza Rock Mechanics Division School of Engineering Tarbiat Modares University Tehran Iran Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Civil Engineering Department School of Engineering Islamic Azad University Larestan Iran

Determining the mechanical properties of plastic concrete samples through experimental investigation is costly and time-consuming. This research used supervised machine learning (ML) techniques, including Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boost (XGBoost), Support Vector machine (SVM), and K-Nearest Neighborhood (KNN) for predicting the compressive strength of the plastic concrete samples considering different values of cement, water, water-to-cement ratio, bentonite, temperature, and sand. The models' performances are compared and evaluated using the correlation of coefficient (R ²) score, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). According to the results, the DT model was more effective in predicting with R ² = 0.87. In addition, a sensitivity analysis was carried out to determine each parameter's contribution level in implementing models using the RF algorithm. Consequently, it was shown that ML techniques are valuable tools for predicting the mechanical properties of plastic concrete in a more time and cost-effective way compared to laboratory tests. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength machine learning algorithm Plastic concrete Regression

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Comparison of the effectiveness of Extended Kalman Filter and machine learning algorithms in sensor fusion 24

Comparison of the effectiveness of Extended Kalman Filter an...

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4th International Conference on Computer, Internet of Things and Control Engineering

作者： Wang, Yijie Wuhan Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9798400711848

On account of the wide application of the autonomous vehicle system, accurate and reliable location information is of great significance. Since the GPS and the IMU information doesn't perform well on their own respectively, however when merging the GPS and the IMU data from the sensors, the high precision of the navigation system is achieved. While the Extended Kalman Filter is the most prevalent in fusing the data, in this paper, algorithms of machine learning, the Random Forest Regression, the Decision Tree Regression, the Multiple Linear Regression and the Extreme Gradient Boosting for instance, algorithms of neural network, the Multiple Perceptron Neural Network for instance is applied for sensor fusion. The results from the experiment demonstrate a promising direction for machine learning and neural network in application in the sensor merging field.

关键词： Autonomous vehicle navigation system Sensor fusion Extended Kalman Filter machine learning algorithm Neural network

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Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

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PEERJ COMPUTER SCIENCE 2022年 8卷 e898页

作者： Islam, Saima Sharleen Haque, Md Samiul Miah, M. Saef Ullah Bin Sarwar, Talha Nugraha, Ramdhan Amer Int Univ Bangladesh AIUB Fac Sci & Technol Dept Comp Sci Dhaka Bangladesh Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pahang Malaysia Telkom Univ Fac Elect Engn Bandung Indonesia

Thyroid disease is the general concept for a medical problem that prevents one's thyroid from producing enough hormones. Thyroid disease can affect everyone-men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine's machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that theANNClassifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.

关键词： Thyroid Disease Thyroid Risk Prediction machine learning algorithm machine learning Classifier Sick-euthyroid

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RETRACTED: Performance analysis of machine learning algorithms in heart disease prediction (Retracted Article)

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第4期30卷 335-343页

作者： Dhasaradhan, K. Jaichandran, R. Vinayaka Missions Res Fdn Deemed Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor Tamil Nadu India

This work presents performance analysis of machine learning algorithms such as logistic regression, naive bayes, decision tree, k nearest neighbour, random forest, support vector machine, and extreme gradient boosting in heart disease prediction. machine learning algorithms are implemented in python using Scikit learn library in Jupiter notebook. Experiments are conducted by training and testing machine learning algorithms using kaggle heart disease dataset under six test cases. Performance of machine learning algorithms are evaluated using accuracy, precision, recall, F1 score and ROC as metrics. Results show random forest reported high accuracy, precision, recall, F1 score and ROC in heart disease prediction compared to other machine learning algorithms in all six test cases. Results show RF is effective in heart disease prediction in Case 3 with 80% train data and 20% test data.

关键词： heart disease prediction machine learning algorithm random forest algorithm train data and test data

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Combining Sample Plot Stratification and machine learning algorithms to Improve Forest Aboveground Carbon Density Estimation in Northeast China Using Airborne LiDAR Data

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REMOTE SENSING 2022年第6期14卷 1477-1477页

作者： Chen, Mingjie Qiu, Xincai Zeng, Weisheng Peng, Daoli Beijing Forestry Univ Coll Forestry State Forestry & Grassland Adm Key Lab Forest Res Beijing 100083 Peoples R China Hainan Univ Coll Forestry Intelligent Forestry Key Lab Haikou City Haikou 570228 Hainan Peoples R China Natl Forestry & Grassland Adm Acad Inventory & Planning Beijing 100714 Peoples R China

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R-2 = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBo

关键词： aboveground carbon density LiDAR stratified estimation machine learning algorithm Northeast China

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Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

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CORROSION SCIENCE 2022年 206卷

作者： Bahrebar, Sajjad Homayoun, Sajad Ambat, Rajan Tech Univ Denmark Dept Civil & Mech Engn Sect Mat & Surface Engn Ctr Elect Corros DK-2800 Lyngby Denmark Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

A printed circuit board (PCB) surface can fail by corrosion due to various environmental factors. This paper focuses on machine learning (ML) techniques to build predictive models to forecast PCB surface failure due to electrochemical migration (ECM) and leakage current (LC) levels under corrosive conditions containing the combination of six critical factors. The modeling methodology in this paper used common supervised ML algorithms by accomplishing significant evaluation metrics to show the performance of each algorithm. The conclusion of this study presents that ML algorithms can create predictive models to forecast PCB failures and estimate LC values effectively and quickly.

关键词： machine learning algorithm Classification Regression Predictive analytics PCB failure Leakage current

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