Predicting climate change impacts on the distribution of endemic fish Cyprinion muscatense in the Arabian Peninsula by Ali Gholamhosseini, Masoud Yousefi, Hamid Reza Esmaeili

“…We used an ensemble approach by considering two regressions‐based species distribution modeling (SDM) algorithms: generalized linear models (GLM), and generalized additive models (GAM) to model the species habitat suitability and predict the impacts of climate change on the species habitat suitability. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…In this paper, we propose a unique approach that utilizes DistilBERT, a distilled version of the Bidirectional Encoder Representations from Transformers, for cancer classification and prediction. In addition, our model integrates a self-attention mechanism in the transformer layers to enhance the model’s focus on key features and employs an embedding layer for dimensionality reduction, improving the processing of gene statistics, preventing overfitting, and boosting generalization. …”
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Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding by Yani Zhang, Qiankun Li, Haijun Duan, Liang Tan, Ying Cao, Junxin Chen

“…This study involved a large cohort of 56,878 hospitalized patients, and we leveraged the XGBoost algorithm to establish a predictive model based on these features. …”
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Evaluation of predictive performance of modeling hyperuricemia using medical big data: comparison of data preprocessing methods by Luwei Li, Xian Huang, Cijin Yan, Shuzhan He, Sishuai Cheng, WenJie Yang

“…Then, the continuous variables in the raw data were assigned values to become categorical variables, and statistical analysis was performed using the same algorithm to obtain the predicted values of the two models. …”
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Autonomic nervous system development-related signature as a novel predictive biomarker for immunotherapy in pan-cancers by Cunen Wu, Cunen Wu, Cunen Wu, Cunen Wu, Weiwei Xue, Yuwen Zhuang, Dayue Darrel Duan, Dayue Darrel Duan, Zhou Zhou, Zhou Zhou, Xiaoxiao Wang, Zhenfeng Wu, Jin-yong Zhou, Xiangkun Huan, Ruiping Wang, Haibo Cheng, Haibo Cheng

“…Differentially expressed genes (DEGs) were validated using RT-PCR and immunohistochemical (IHC) analyses of clinical samples.ResultsAnalysis of scRNA-seq datasets and autonomic nervous system development (ANSD) scores revealed 20 genes comprising a novel ANSD-related differential signature (ANSDR.Sig). A pan-cancer predictive model for ICI prognosis based on ANSDR.Sig was constructed, with the random forest algorithm yielding the most robust performance. …”
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Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study by Girish N Nadkarni, Orly Efros, Eyal Klang, Shelly Soffer, Gili Kenet, Aya Mudrik, Renana Robinson

“…The primary hypothesis was that an ML model could achieve better accuracy in identifying low ferritin levels (<30 ng/mL) in non-anaemic patients compared with traditional methods.Design A retrospective cohort study.Setting Data were derived from secondary and tertiary care facilities within the eight-hospital Mount Sinai Health System, an urban academic health system.Participants The study included 211 486 adult patients (aged ≥18 years) with normal haemoglobin levels (≥130 g/L for men and ≥120 g/L for women) and recorded ferritin measurements.Primary and secondary outcome measures The primary outcome was the prediction of low ferritin levels (<30 ng/mL) using extreme gradient-boosted decision trees, an ML algorithm suited for structured clinical data. …”
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Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models by Chuanbin Chen, Qingmin Chen, Shenghai Liu, Guoxi Li, Jiawei Zhao, Jingting Huang, Tianyi Ye, Xinting Yang, Zigui Huang, Zhen Wang, Fuhai He, Mingjian Qin, Chenyan Long, Binzhe Tang, Yongqi Huang, Weizhong Tang, Jungang Liu, Xiaoliang Huang

“…Finally, we successfully developed a predictive model to predict PNI in CRC patients through leveraging microbial biomarkers. …”
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Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. by Daniele Da Re, Marius Gilbert, Celia Chaiban, Pierre Bourguignon, Weerapong Thanapongtharm, Timothy P Robinson, Sophie O Vanwambeke

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Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study. by Zhi Nie, Srinivasan Vairavan, Vaibhav A Narayan, Jieping Ye, Qingqin S Li

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Machine learning approaches for predicting the link of the global trade network of liquefied natural gas. by Pei Zhao, Hao Song, Guang Ling

“…The findings indicate that random forest and decision tree algorithms, when used with local similarity-based indices, demonstrate strong predictive performance. …”
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Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

“…Given that machine learning algorithms often surpass the precision of remote sensing technology, further exploration of machine learning algorithms in the development of sugarcane yield prediction models is imperative. …”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…<h4>Background</h4>Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. …”
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A Novel approach to ship valuation prediction: An application to the supramax and ultramax secondhand markets. by Elif Tuçe Bal, Ercan Akan, Huseyin Gencer

“…(ii) For the two linear regression models created; Price predictions were made with Linear Regression, Decision Tree, Random Forest and XGBoost ML algorithms. …”
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Exploring the application of machine learning and SHAP explanations to predict health facility deliveries in Somalia by Jamilu Sani, Salad Halane, Mohamed Mustaf Ahmed, Abdiwali Mohamed Ahmed, Jamal Hassan Mohamoud

“…Methods This study analyzed data from the 2020 Somalia Demographic and Health Survey (SDHS) involving 8,951 women aged 15–49 years. Seven ML algorithms, Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbors, were evaluated for their ability to predict health facility deliveries. …”
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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

“…The characteristics are subsequently used to educate five machine learning algorithms, namely k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), XGBoost, and random forest (RF). …”
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Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates by Nazareno Gonzalez, Melanie Pérez Küper, Matías Garcia Fallit, Jorge A. Peña Agudelo, Alejandro Nicola Candia, Maicol Suarez Velandia, Ana Clara Romero, Guillermo Videla Richardson, Marianela Candolfi

“…The model was used to predict GBM sensitivity to various drugs, which was then validated using GBM cellular models. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…This study aimed to create a predictive risk score using an ARGs signature for NPC patients and to investigate how this score relates to clinicopathologic features and immune infiltration in the tumor microenvironment. …”
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Machine learning based adaptive traffic prediction and control using edge impulse platform by Manoj Tolani, G. E. Saathwik, Ayush Roy, L. A. Ameeth, Dhanush Bharadwaj Rao, Ambar Bajpai, Arun Balodi

“…A Edge-Impulse-based machine learning model is proposed to predict the density and arrival time of the vehicles to the traffic signal. …”
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Harnessing artificial intelligence role in oral cancer diagnosis and prediction: A comprehensive exploration by Archana Behera, N. Aravindha Babu, Remya Rajan Renuka, Mukesh Kumar Dharmalingam Jothinathan

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Comprehensive comparison between artificial intelligence and multiple regression: prediction of Palmerston North’s temperature by M. Y. Tufail, S. Gul

“…We found that all three algorithms performed well, successfully predicting the desired temperature data. …”
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