Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…To respond to and address this challenge more effectively, a predictive, data-driven approach is essential. This study applies machine learning techniques to forecast the number of people living in poverty in 2024 at the district/city level, utilizing socio-economic data from 2019 to 2023 provided by the Central Bureau of Statistics (BPS). …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.…”
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A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments by Jae Kwon Bae, Jinhwa Kim

“…This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. …”
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Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations by Ayoub Jadouli, Chaker El El Amrani

“…We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. …”
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Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing. by Sven B Gould, Jonas Magiera, Carolina García García, Parth K Raval

“…Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. …”
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Link prediction accuracy on real-world networks under non-uniform missing-edge patterns. by Xie He, Amir Ghasemian, Eun Lee, Alice C Schwarze, Aaron Clauset, Peter J Mucha

“…To investigate the impact of different missing-edge patterns on link prediction accuracy, we employ 9 link prediction algorithms from 4 different families to analyze 20 different missing-edge patterns that we categorize into 5 groups. …”
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Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics by Laura Hannemose Rieger, Klemen Zelič, Igor Mele, Tomaž Katrašnik, Arghya Bhowmik

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Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis by Zohreh Javanmard, Saba Zarean Shahraki, Kosar Safari, Abbas Omidi, Sadaf Raoufi, Mahsa Rajabi, Mohammad Esmaeil Akbari, Mehrad Aria

“…Original articles applying ML algorithms for BC survival prediction using clinical data were included. …”
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Machine Learning-Based Prediction of Ecosystem-Scale CO₂ Flux Measurements by Jeffrey Uyekawa, John Leland, Darby Bergl, Yujie Liu, Andrew D. Richardson, Benjamin Lucas

“…In this study, we use machine learning algorithms to predict CO₂ flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. …”
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An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability by Liu A, Zhang Y, Yadav CP, Chen W

“…Anqi Liu, Yue Zhang, Chandra Prakash Yadav, Wenjia Chen Saw Swee Hock School of Public Health, National University of Singapore, SingaporeCorrespondence: Wenjia Chen, Tahir Foundation Building, National University of Singapore, 12 Science Drive 2, #10-01, Singapore, 117549, Email wenjiach@nus.edu.sgBackground: Accurate risk prediction of exacerbations in asthma patients promotes personalized asthma management.Objective: This systematic review aimed to provide an update and critically appraise the quality and usability of asthma exacerbation prediction models which were developed since 2017.Methods: In the Embase and PubMed databases, we performed a systematic search for studies published in English between May 2017 and August 2023, and identified peer-reviewed publications regarding the development of prognostic prediction models for the risk of asthma exacerbations in adult patients with asthma. …”
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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

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Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning by Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

“…This study aims to analyze the influence of academic and non-academic factors on career alignment and to develop a predictive model using machine learning algorithms. …”
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Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Abstract This study investigates the use of machine learning (ML) models to predict the ultimate load capacity of demountable shear connectors in steel–concrete composite structures. …”
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Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning by Jitao Zhao, Miaomiao Zhu, Lidan Xu, Ming Chen, Mingfang Shi

“…Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. …”
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Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study by Shanshan Jin, Xu Zhang, Hanruo Liu, Jie Hao, Kai Cao, Caixia Lin, Mayinuer Yusufu, Na Hu, Ailian Hu, Ningli Wang

“…To identify an optimal model for diabetic retinopathy (DR) prediction in Chinese rural population by establishing and comparing different algorithms based on the data from Handan Eye Study (HES). …”
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A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease by Ji Seung Ryu, Jang Hoon Ru, Yunkoo Kang, Sejung Yang

“…With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable attention, with the aim of preemptively managing fluctuations before they become problematic. …”
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Efficient neural network training method for unsteady flow field prediction based on data pool by Qiuchi Min, Tianyu Li, Guanxiong Li, Shuyuan Liu, Laiping Zhang, Xiaogang Deng

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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. After selecting the best algorithm, postoperative ICU admission probability prediction nomogram was constructed. …”
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Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data by Gabriel Ferreira dos Santos Silva, Roberta Moreira Wichmann, Francisco Costa da Silva Junior, Alexandre Dias Porto Chiavegatto Filho

“…Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabling precise prediction and prevention of mortality risks. …”
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