WCN25-867 BAYESIAN DEEP LEARNING ALGORITHMS TO PREDICT THE RATE OF DETERIORATION IN CHRONIC KIDNEY DISEASE by Laxmi Priya Kasi, Sujoy Kar, Sanjay Maitra, Tameem Ansari, Bharath Potla, Sangita Reddy

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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

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PM2.5 concentration prediction using machine learning algorithms: an approach to virtual monitoring stations by Ahmad Makhdoomi, Maryam Sarkhosh, Somayyeh Ziaei

“…The ML models performed very well in predicting the concentrations of PM2.5 with around 95% of their predictions falling within the factor of the observed concentration. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. …”
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Leveraging Machine Learning Regression Algorithms to Predict Mechanical Properties of Evaporitic Rocks From Their Physical Attributes by Ayham Zaitouny, Hasan Arman, Anusuya Krishnan, Alaa Ahmed, Ahmed Gad

“…In this study, machine learning (ML) regression algorithms were applied to predict four key mechanical parameters, namely, uniaxial compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), and Schmidt hardness value (SHV), based on the physical attributes of evaporitic rocks. …”
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The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

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Development and Validation of a Neonatal Hypothermia Prediction Model for In-Hospital Transport Using Machine Learning Algorithms: A Single-Center Retrospective Study by Zhang W, Gu X, Gu C, Yao L, Zhang Y, Wang K

“…Six machine learning algorithms—Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naive Bayes (NB)—were used to develop predictive models. …”
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Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction by Yupeng Li, Ke Sun

“…Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classification model (RFC), The Grasshopper Optimizer Algorithm (GOA), and the Artificial Rabbits Optimizer (ARO). …”
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Excavating trajectory planning of electric shovel based on dynamic excavating volume prediction by Juan Wu, Rui Liu, Baoguo Lin

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Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China by Luyao Zhou, Weimin Zhang, Min Shao, Cui Wang, Yu Wang

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Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm by Qianmei Yang, Cuifang Liu, Yongyue Wang, Guifang Dong, Jinghuan Sun

“…We than successfully leveraged machine learning algorithms, particularly the RANDOM FOREST model, to develop a predictive model for sentinel lymph node metastasis in breast cancer. …”
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INTELLIGENT SYSTEM FOR CLASSIFICATION OF STUDENT PERSONALITY WITH NAIVE BAYES ALGORITHM by Dony Fahrudy, Izza Afkarina, Muhammad Fadli, Rinny Asasunnaja, Wildan Nadiyal Ahsan, Febri Eka Setyawan, Maria Ulfah Siregar

“…The data held are then calculated using the nave Bayes algorithm. Based on the results, there were 12 students correctly predicted and 1 students did not predict correctly so that an accuracy of 92.31% was obtained with an error rate of 7.69%. …”
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Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development by Biplov Paneru, Durga Prasad Mainali, Bishwash Paneru, Sanjog Chhetri Sapkota

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Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms by Bruna Carbas, Pedro Sampaio, Sílvia Cruz Barros, Andreia Freitas, Ana Sanches Silva, Carla Brites

“…This study evaluates the potential of near-infrared (NIR) spectroscopy combined with chemometric algorithms to detect fumonisins in maize. For fumonisin B1 (FB1) and B2 (FB2) levels were developed predictive NIR models using partial least squares (PLS) and artificial neural networks (ANN). …”
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Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning by Mohd Naved, Ravi Kumar, Shaiku Saheb

“…Machine learning (ML) may help in bankruptcy prediction by analyzing massive quantities of historical financial data, identifying trends and anomalies that indicate trouble, and developing predictive models to estimate the possibility of default. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
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Energy Consumption Prediction in Iran: A Hybrid Machine Learning and Genetic Algorithm Method with Sustainable Development Considerations by Seyyed Mohammad Mehdi Fatemi Bushehri, Saeed Dehghan Khavari, Seyed Hossein Mirjalili, Hamid Babaei Meybodi, Mohsen Sardari Zarchi

“…The algorithm was able to select 14 features as the most effective indicators in predicting energy consumption from all the 104 ones in the IREC with 500 repetitions. …”
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Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model by Binhui Ma, Yarui Xiao, Tian Lan, Chao Zhang, Zengliang Wang, Zeshi Xiang, Yuqi Li, Zijing Zhao

Subjects: “…settlement prediction…”
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Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study by Jialu Li, Yi Ding, Yiwei Hao, Chengyu Gao, Jinjing Xiao, Ying Liu, Yining Zhao, Qinlan Li, Lulu Xing, Hongyuan Liang, Liang Ni, Fang Wang, Sa Wang, Di Yang, Guiju Gao, Jiang Xiao, Hongxin Zhao

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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

“…In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. …”
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