Development and validation of a machine learning model for prediction of cephalic dystocia by Yumei Huang, Xuerong Ran, Xueyan Wang, Defang Wu, Zheng Yao, Jinguo Zhai

“…The least absolute shrinkage and selection operator (LASSO) algorithm was used to select predictive factors, followed by the development of logistic regression, decision tree, and random forest machine learning models. …”
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Mathematical model for predicting the performance of photovoltaic system with delayed solar irradiance by Siti Nurashiken Md Sabudin, Norazaliza Mohd Jamil

“…The goal of this study was to develop a mathematical model for predicting the performance of a photovoltaic system, which depends on the amount of solar irradiance. …”
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Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models by Wenqing Li, Jing Lv, Yuhang Wang, Xiangfeng Kong

“…The intelligent parameter optimization framework proposed in this study provides theoretical support for the development of a marine ranching DO monitoring system, and its technical path can be extended to the prediction of other water environment indicators. Future research will develop a parameter adaptive selection algorithm, conduct the dynamic monitoring of multi-scale environmental factors, and achieve the intelligent optimization and verification of model parameters.…”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…Abstract MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations. …”
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Predicting patients’ sentiments about medications using artificial intelligence techniques by Amir Sorayaie Azar, Samin Babaei Rikan, Amin Naemi, Jamshid Bagherzadeh Mohasefi, Uffe Kock Wiil

“…Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients’ sentiments. This study used a large medication review dataset to perform a SA of medications. …”
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Lifestyle factors and colorectal cancer prediction: A nomogram-based model by Wooin Seo, Se Young Jung, Yeonhoon Jang, Kiheon Lee

“…Using the LASSO regression algorithm, we selected risk factors and fitted a Cox proportional hazards model to predict the 10-year CRC incidence. …”
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Machine learning for prediction of childhood mental health problems in social care by Ryan Crowley, Katherine Parkin, Emma Rocheteau, Efthalia Massou, Yasmin Friedmann, Ann John, Rachel Sippy, Pietro Liò, Anna Moore

“…This is particularly important for children with social care contact because earlier identification can facilitate earlier intervention. Clinical prediction tools could improve these early intervention efforts. …”
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Neural network-based performance prediction of marine UHPC with coarse aggregates by Yunhao Luan, Dongbo Cai, Deming Wang, Changqing Luo, Anni Wang, Chao Wang, Degao Kong, Chaohui Xu, Sining Huang

“…In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). …”
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Predicting the risk of hematoma expansion in acute intracerebral hemorrhage: the GIVE score by Tian-Nan Yang, Xin-Ni Lv, Zi-Jie Wang, Xiao Hu, Li-Bo Zhao, Jing Cheng, Qi Li

“…Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients. …”
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Predicting the presence of adjacent septic arthritis in children with acute hematogenous osteomyelitis by Shuting Lin, Donghao Gu, Peng Ning, Jingyu Wu, Zhixin Yang, Tianjing Liu

“…Four risk factors (age below 4 years, a history of preceding infection, platelet count > 390.5 × 10^9/L, and absolute neutrophil count < 5.45 × 10^3 cells/ml) were found to be predictive of concomitant infection and were included in the algorithm. …”
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Corticosteroid treatment prediction using chest X-ray and clinical data by Anzhelika Mezina, Samuel Genzor, Radim Burget, Vojtech Myska, Jan Mizera, Aleksandr Ometov

“…Conclusions: The introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. …”
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Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives by Kainat Iqbal, Bayan Alabdullah, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Jeongmin Park

“…Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. …”
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Predicting breeding values in Murrah buffaloes using random regression models by ANAND JAIN, ASHISH RANJAN, I D GUPTA, H K SINGH

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Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning by Sheila Santa, Samuel Kojo Kwofie, Kwasi Agyenkwa-Mawuli, Osbourne Quaye, Charles A Brown, Emmanuel A Tagoe

“…This study aimed to develop a deep learning model to predict interactions between HPV and host proteins. …”
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Construction of a prediction model for sarcopenic obesity based on machine learning by Mengru Xu, Mengru Xu, Jia Liu, Jia Liu, Song Hu, Song Hu, Tongxiao Luan, Tongxiao Luan, Yuting Duan, Yuting Duan, Aohua Wang, Aohua Wang, Ziwei Cui, Ziwei Cui, Jing Zhou, Yongjun Mao, Yongjun Mao

“…We identified four independent predictive factors, namely BMI, Barthel Index score, grip strength, and calf circumference. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…Compared with previous studies, a more stable water content prediction model of Anshan magnetite was constructed by combining data preprocessing, CARS feature screening and nonlinear regression algorithm, which provides higher precision support for water content detection in mining production.…”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. …”
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Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice by Jie Zhou

“…For selecting the most relevant features, we utilized the Minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Elimination (RFE) algorithms. Following this, we developed and assessed Support Vector Machine (SVM) and Logistic Regression (LR) models, measuring their performance through various metrics such as accuracy, specificity, sensitivity, positive predictive value, calibration curves, the Hosmer–Lemeshow goodness-of-fit test, decision curve analysis (DCA), and Kaplan–Meier survival analysis. …”
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Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively by LI Qiang, LI Qiang, MU Guo, MU Guo, WANG Wenzhang

“…After data cleaning and preprocessing, Boruta feature selection was applied, followed by the construction of prediction models using 7 machine learning algorithms, that is, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), k-Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NNET), Support Vector Machine (SVM), and XGBoost. …”
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Long Short-Term Memory–Model Predictive Control Speed Prediction-Based Double Deep Q-Network Energy Management for Hybrid Electric Vehicle to Enhanced Fuel Economy by Haichao Liu, Hongliang Wang, Miao Yu, Yaolin Wang, Yang Luo

“…The initial learning rate and dropout probability of the LSTM speed prediction model are optimized using a Double Deep Q-Network (DDQN) algorithm. …”
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