Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis by Zhijian Ren, Minqiao Zhang, Pingping Wang, Kanan Chen, Jing Wang, Lingping Wu, Yue Hong, Yihui Qu, Qun Luo, Kedan Cai

“…Utilizing machine learning to predict blood pressure fluctuations during dialysis has become a viable predictive method. …”
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Development of a deep learning system for predicting biochemical recurrence in prostate cancer by Lu Cao, Ruimin He, Ao Zhang, Lingmei Li, Wenfeng Cao, Ning Liu, Peisen Zhang

“…Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms. Results The BCR prediction system demonstrated great performance in the testing cohort (AUC = 0.911, 95% Confidence Interval: 0.840–0.982) and showed the potential to produce favorable clinical benefits according to Decision Curve Analyses. …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…By comparing the predictive performance of different algorithms, we aimed to establish a robust tool to identify patients most likely to benefit from BPA. …”
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Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al‐Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…We developed machine learning algorithms that predict 1‐year stroke or death following TFCAS. …”
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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…This study presents a novel dual approach for diabetes risk prediction in women, combining machine learning classification with fractional-order physiological modeling. …”
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IVIM-DWI-based radiomic model for preoperative prediction of hepatocellular carcinoma differentiation by ZHUANG Yuxiang, LI Xiaofeng, ZHOU Daiquan

“…Objective To construct a radiomic model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for preoperative prediction of hepatocellular carcinoma (HCC) differentiation and validate its clinical value. …”
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The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer by Qiujun He, Xiangxing Kong, Xiangxi Meng, Xiuling Shen, Nan Li

“…The AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of XGBoost’s internal and external validation were 0.945, 0.932, 0.930, 0.960, 0.970, 0.890 and 0.910, 0.900, 0.860, 1, 1, 0.750. …”
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Prediction on rock strength by mineral composition from machine learning of ECS logs by Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan

“…This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. …”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients. …”
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Triphasic CT Radiomics Model for Preoperative Prediction of Hepatocellular Carcinoma Pathological Grading by Huang H, Pan X, Zhang Y, Yang J, Chen L, Zhao Q, Huang L, Lu W, Deng Y, Huang Y, Ding K

“…Preoperative prediction of HCC pathological features (Ed, MVI, and SN grading) is clinically significant.A triphasic CT-based fusion model demonstrated strong predictive performance:Testing 1 dataset: AUCs of 0.890 (Ed), 0.895 (MVI), and 0.829 (SN) grading.Testing 2 (validation) dataset: AUCs of 0.836 (Ed), 0.871 (MVI), and 0.810 (SN) grading.The model aids in preoperative clinical decision-making and prognostic evaluation for HCC patients.Keywords: pathological grading, hepatocellular carcinoma, contrast-enhanced CT, radiomics…”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), sensitivity, specificity, and Mathew’s correlation coefficient (MCC).ResultsWe trained and evaluated eight different ML algorithms, including logistic regression, support vector machines, Naïve Bayes, k-nearest neighbors, decision trees, random forests, AdaBoost, and gradient-boosting classifiers. …”
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The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal valid... by Janny Xue Chen Ke, Arunachalam DhakshinaMurthy, Ronald B. George, Paula Branco

“…Conclusion Existing resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.…”
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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…After training and optimizing multiple ML algorithms, we generated a model with the highest area under the receiver operating characteristic curve (AUROC) to predict short-term outcomes following DCM surgery. …”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
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Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol by Sergi Mas, Sergi Mas, Sergi Mas, Laura Julià, Manuel J. Cuesta, Manuel J. Cuesta, Manuel J. Cuesta, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Carlos Spuch, Carlos Spuch, Ana Gonzalez-Pinto, Ana Gonzalez-Pinto, Angela Ibañez, Angela Ibañez, Judith Usall, Judith Usall, Cristina Romero-López-Alberca, Cristina Romero-López-Alberca, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Anna Mané, Anna Mané, Anna Mané, Anna Mané, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo

“…Here, we describe the rationale, aims and methodology of Applied Pharmacogenetics to Predict Response to Treatment of First Psychotic Episode (the FarmaPRED-PEP project), which aims to develop and validate predictive algorithms to classify FEP patients according to their response to antipsychotics, thereby allowing the most appropriate treatment strategy to be selected. …”
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A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools by Rifa Andriani Saputri, Asrianda Asrianda, Lidya Rosnita

“…The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. …”
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Safety Prejudging Method for Power Transformer Based on Multi-Prediction Model Fusion by Dianyang LI, Yujie ZHANG, Shanyuan WANG, Jian FENG, Hongzhe WANG, Ling QIN

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Prediction of moisture content of hummus peach based on multi-burr hyperspectral data by GAO Aidi, QIAO Fengzhang, ZHU Wenxuan, ZHONG Xiaopin, DENG Yuanlong

“…For hyperspectral image data with spikes and noise, compared the effects of several data preprocessing methods, including polynomial smoothing algorithm (SG), multivariate scatter correction algorithm (MSC), standard normal variate algorithm (SNV), first-order derivative operator (D1), and second-order derivative operator (D2) on model prediction accuracy. …”
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Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery by Ran Li, Hongbing Liu, Rui Xue, Yanling Li

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PREDICTION STAGES OF THE DIFFICULT INERTIA SYSTEM BEHAVIOR WITH THE USE OF THE DEVELOPED SYSTEM MODEL by Vasyl V. Bychenkov

“…The model creation and use algorithm for prediction of the difficult inertia system behavior is offered in the article. …”
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