Predicting in-hospital mortality in ICU patients with Coronary heart disease and diabetes mellitus using machine learning models by Guang Tu, Zhonglan Cai, Ling Wu, Hang Yu, Hongke Jiang, Haijian Luo

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Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars by Giuseppe Montanaro, Antonio Carlomagno, Angelo Petrozza, Francesco Cellini, Ioanna Manolikaki, Georgios Koubouris, Vitale Nuzzo

“…The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). …”
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Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis by Yanan Wang, Zengyi Zhang, Zhimeng Zhang, Xiaoying Chen, Junfeng Liu, Ming Liu

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Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties by Amir Hossein Sheikhshoaei, Ali Khoshsima

“…These findings highlight the effectiveness of using critical properties as inputs and underscore the potential of the LightGBM model for reliable high-pressure density prediction of thiophene derivatives. …”
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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome by Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán

“…Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid leukemia (AML) using data available at diagnosis. …”
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Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models by Azita Molaeinasab, Hossein Bashari, Mostafa Tarkesh Esfahani, Saeid Pourmanafi, Norair Toomanian, Bahareh Aghasi, Ahmad Jalalian

“…We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. …”
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ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils by Mehdi Hashemi Jokar, Ali Heidaripanah

“…This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. …”
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Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking by Tommy Sugiarto, Yi-Jia Lin, Hsiao-Liang Tsai, Chi-Tien Sun, Wei-Chun Hsu

“…Abstract Background Wearable sensors combined with deep-learning models are increasingly being used to predict biomechanical variables. …”
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Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study by Sultan Almuaythir, Muhammad Syamsul Imran Zaini, Rida Hameed Lodhi

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A Hybrid Internet of Behavior Algorithm for Predicting IoT Data of Plant Growing using LSTM and NB Models by Khansaa Yaseen Ahmad, Omar Muayad Abdullah

“…This research proposes a hybrid Internet of Behaviors (IoB) technique that linking between time-series predicting and the classification models to estimate the plant growing behaviors using real environmental data. …”
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Comparison and evaluation of different constitutive models for predicting the hot deformation behavior of Mg-Gd-Y-Zr alloy by Yanbo Pei, Liting Li, Menghua Yu, Enbo Wei, Maosheng Zhao, Bugang Teng

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Assessing the effectiveness of fuzzy logic-based models for predicting sports event outcomes: A CRITIC-VIKOR approach by Taibo Liu

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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. …”
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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance.…”
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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study by Zhaobin He, Shengbiao Yang, Jianqiang Cao, Huijie Gao, Cheng Peng

“…This study aimed to develop and validate interpretable machine learning (ML) models to accurately predict neoplastic GBPs in a retrospective cohort, identifying key features and providing model explanations using the Shapley additive explanations (SHAP) method. …”
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X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head by Yaqing He, Yang Chen, Yusen Chen, Pingshi Li, Le Yuan, Maoxiao Ma, Yuhao Liu, Wei He, Wu Zhou, Leilei Chen

“…Abstract This study aimed to develop an X-ray radiomics model for predicting collapse of early-stage osteonecrosis of the femoral head (ONFH). …”
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Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study by Mingfei Jiang, Xiaoran Li, YongLu

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Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models by Behnam Amiri-Ramsheh, Sara Sahebalzamani, Reza Zabihi, Abdolhossein Hemmati-Sarapardeh

“…This research aims to accurately predict asphaltene precipitation values using an extensive databank containing 380 experimental data points. …”
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Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients by Amirmohammad Toloui, Amir Ghaffari Jolfayi, Hamed Zarei, Arash Ansarian, Amir Azimi, Seyed Mohammad Forouzannia, Rosita Khatamian Oskooi, Gholamreza Faridaalaee, Shayan Roshdi Dizaji, Seyed Ali Forouzannia, Seyedeh Niloufar Rafiei Alavi, Mohammadreza Alizadeh, Hadis Najafimehr, Saeed Safari, Alireza Baratloo, Mostafa Hosseini, Mahmoud Yousefifard

“…These models may reduce unnecessary CT scans and optimize resource use. …”
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On the Sources and Sizes of Uncertainty in Predicting the Arrival Time of Interplanetary Coronal Mass Ejections Using Global MHD Models by Pete Riley, Michal Ben‐Nun

“…The ICME's time of arrival (ToA) at Earth is an important parameter, and one that is amenable to a variety of modeling approaches. Previous studies suggest that the best models can predict the arrival time to within an absolute uncertainty of 10–15 h. …”
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