A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression by BA Mengru, YIN Xiaohong, LI Shaoyuan

“…Parkinson's disease (PD) patients were often accompanied by cognitive impairment, which seriously affected the quality of life, so the over-prediction of cognitive impairment in Parkinson's disease was crucial for clinical diagnosis and intervention. …”
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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization by Tianhao Yan, Xiaomeng Xu, Jiafeng Liu, Yihuai Zhang, Muhammad Arif, Xiaowei Xu, Qiang Wang

“…The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability. …”
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Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease by Guangda Xin, Guangyu Zhou, Wenlong Zhang, Xiaofei Zhang

“…Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD. …”
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Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning by Chun-Wei Chang, Chien-Hung Chang, Chia-Yin Chien, Jian-Lin Jiang, Tsai-Wei Liu, Hsiu-Chuan Wu, Kuo-Hsuan Chang

“…However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed the Gradient Boosting matching learning algorithm to establish predictive models for HAI occurrence in AIS patients and poor 30-day functional outcomes (modified Rankin Scale > 2) in AIS patients with HAI by analyzing electronic health records from 6560 AIS patients. …”
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Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…Objective We evaluated the predictive value of electrocardiogram (ECG)–derived features and developed an ML model to stratify HF risk. …”
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Predictive factors of cardiovascular changes depending on the type and intensity of physical activity in professional athletes by N. P Garganeeva, I. F. Taminova, V. V. Kalyuzhin, E. V Kalyuzhina, I. N. Smirnova

“…To determine the early predictive factors of cardiovascular changes in professional athletes, depending on the type and intensity of physical activity.Material and methods. …”
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Maternal plasma cell-free RNA as a predictive test for fetal lung maturation by Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

“…Delivery and ventilation data were analyzed with ANOVA, Tukey HSD, and Dunnett T3 tests. A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. …”
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Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: a multi-centre study by Shuai Yang, Shuai Yang, Shuang Leng, Shuang Leng, Zhouchi Wang, Jiang Ming Fam, Jiang Ming Fam, Adrian Fatt Hoe Low, Adrian Fatt Hoe Low, Ru-San Tan, Ru-San Tan, Ping Chai, Ping Chai, Lynette Teo, Lynette Teo, Chee Yang Chin, Chee Yang Chin, John C. Allen, Mark Yan-Yee Chan, Mark Yan-Yee Chan, Khung Keong Yeo, Khung Keong Yeo, Aaron Sung Lung Wong, Aaron Sung Lung Wong, Soo Teik Lim, Soo Teik Lim, Qinghua Wu, Liang Zhong, Liang Zhong, Liang Zhong

“…For per-patient analysis, the corresponding values were 85.8%, 85.7%, 86.0% in the development cohort and 82.2%, 83.3%, 81.1% in the validation cohort.ConclusionThe nomogram may be useful for predicting ischemic lesions using QCA measurements and the LASSO regression algorithm, with external validation indicating potential predictive value in cardiology care settings.…”
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Abstract 035: Workflow Improvements from Automated LVO Detection Algorithms Are Dependent on Care Team Engagement by Hussain M Azeem

“…Introduction Automated machine learning (ML)‐based large vessel occlusion (LVO) detection algorithms have been shown to improve in‐hospital workflow metrics including door to groin time (DTG). …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…The second model pairs eXtreme Gradient Boosting (XGBoost), a highly efficient boosting algorithm for tabular data, with an Artificial Neural Network (ANN). …”
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SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models by Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine, Milan Toma

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Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT by Osama Khan, Briya Tariq, Nadine Francis, Nabil Maalej, Abderaouf Behouch, Amer Kashif, Asim Waris, Aamir Raja

“…The proposed model was quantitatively evaluated, with and without the metal artefact reduction (MAR) algorithm, using line profiles, histogram analysis, signal-to-noise ratio (SNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). …”
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Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

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Optimized design of high-strength leaf chain based on response surface methodology by LIU Meiqi, FENG Zengming, ZHOU Zhidi

Subjects: “…Multi-Objective genetic algorithm…”
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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms by Zhong-Han Zhuang, Hui-Ping Tsai, Chung-I Chen

“…Using feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters. …”
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Optimal Integration of Multiple Shunt Reactive Compensators in Radial Distribution Systems for Loss Reduction using Modified Mountain Gazelle Optimizer (MMGO) by A. F. S. Yussif, E. Twumasi, E. A. Frimpong

“…Secondly, the proposed MMGO algorithm is applied to determine the optimal locations and sizes of the shunt compensators. …”
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Terminal sliding mode control with sensor reduction of a permanent magnet synchronous generator supplying a pumping system with battery storage by Clotaire Thierry Sanjong Dagang, Mathieu Jean Pierre Pesdjock, Godpromesse Kenné

“…This work is concerned with the study and development of a terminal sliding mode control process that employs an estimator of the wind speed and the mechanical rotation speed of the permanent magnet synchronous generator (PMSG). This results in a reduction in the overall cost of the wind power system, which comprises a PMSG, a Pulse Width Modulation (PWM) rectifier, a battery, a DC–DC converter, a PWM inverter and a centrifugal pump driven by an asynchronous squirrel-cage machine. …”
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Simultaneous OPEX and carbon footprint reduction with hydrogen enhancement in autothermal reforming: a machine learning–based surrogate modeling and optimization framework by Sahar Shahriari, Davood Iranshahi

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Investigação teórica do processo de redução de coliformes em leitos cultivados Theoretical investigation of coliform reduction in constructed wetland by Mariângela Amendola, Anderson L. Souza

“…To solve the equation an algorithm based on the implicit scheme of the finite difference method was implemented which, when ran with MATLAB 6.1, generated values for the pollutant concentration which depend on the mentioned constants. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Using 12 machine learning algorithms, 110 combination predictive models were constructed. …”
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