A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis by Jiaojiao Xu, Wei Zhang, Weili Bai, Nannan Gai, Jing Li, Yunqi Bao

“…The ILD group exhibited significantly elevated levels of inflammatory markers and specific biomarkers, particularly KL-6 (826.4 ± 458.2 vs. 285.6 ± 124.8 U/ml, P < 0.001), alongside distinct patterns in hematological parameters. Conclusion Machine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. …”
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Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques by Joan D. Gonzalez-Franco, Alejandro Galaviz-Mosqueda, Salvador Villarreal-Reyes, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jose E. Gonzalez-Trejo, Alexei-Fedorovish Licea-Navarro, Jorge Lozoya-Arandia, Edgar A. Ibarra-Flores

“…This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. …”
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A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes by J. Li, A. Geiss, Z. Feng, L. R. Leung, Y. Qian, W. Cui, W. Cui

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Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms by Bowen Zheng, Jianxiong Qiao, Xiaoping Yu, Hanghang Zhou, Anqi Wang, Xuanfen Zhang

“…This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…The Ethiopia Demographic and Health Survey (EDHS-2019), which offers thorough data on socioeconomic, demographic, and water access determinants, was the data source for this study. The following six machine-learning models were used: k-nearest Neighbors, Random Forest, Support Vector Machines, Gradient Boosting Machines, and Artificial Neural Networks. …”
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Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection by Sulandri Sulandri, Achmad Basuki, Fitra Abdurrachman Bachtiar

“…Intrusion detection is the process of monitoring traffic on a network to detect any data patterns that are considered suspicious, which allows network attacks. …”
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Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Results Architecturally simpler embedding models captured biological interaction patterns, often outperforming complex approaches. The two-stage training strategy achieved improvements up to 26.9% for protein-protein interactions. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Although adjustable risk factors—including environmental exposures, sleep disturbances, and dietary patterns—are increasingly implicated in pathogenesis of migraine, their causal roles remain insufficiently characterized, and the integration of multimodal evidence lags behind epidemiological needs. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…Training the models with a larger amount of data allows for capturing more representative patterns, which improves their generalization ability and performance on new data.…”
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Research on the interaction between energy consumption pattern transformation and international trade in the digital economy era based on gig data analysis by Yiqun Wu, Shibing Zhang

“…Abstract With the rapid development of the digital economy, energy consumption patterns are transforming. However, problems remain, such as low energy efficiency and heavy dependence on traditional energy. …”
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Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…<b>Methods:</b> This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. …”
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Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis by Yu Chi, Huai-yu Chen, Khushboo Thaker

“…By uncovering interaction patterns, this study provides valuable insights for leveraging online support groups as complementary resources to traditional recovery interventions.…”
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Big Earth data processing using machine learning for integrated mapping of the dead sea fault, Jordan by Polina Lemenkova

“…The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. …”
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Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications by Ying-Ying Tsai, Yu-Jie Chen, Yen-Feng Lin, Fan-Chi Hsiao, Ching-Han Hsu, Lun-De Liao

“…Furthermore, machine learning models can integrate temporal patterns in HRV data to achieve nuanced stress level assessment. …”
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Big Earth data processing using machine learning for integrated mapping of the dead sea fault, Jordan by Polina Lemenkova

“…The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. …”
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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China by Xinzhi Wang, Qingxia Lin, Zhiyong Wu, Yuliang Zhang, Changwen Li, Ji Liu, Shinan Zhang, Songyu Li

“…This study integrates hydrological modeling, machine learning methods, and long-term agricultural economic data from 1991 to 2020 in the Jialing River Basin (JRB) to detect and forecast meteorological and agricultural droughts, as well as their impact on cropland. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…Abstract In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. …”
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Digital mapping of soil erodibility factor in response to land use change using machine learning models by Wudu Abiye, Orhan Dengiz

“…The study utilized Multiple Linear Regression (MLR) and machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and an ensemble of the four single models. …”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. …”
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A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques by Mohamed J. Saadh, Waleed K. Abdulsahib, Hardik Doshi, Anupam Yadav, J. Gowrishankar, Mayank Kundlas, Nargiza Mansurova, Kamal Kant Joshi, Fadhil Feez Sead, Bagher Farhood

“…It integrates advanced machine learning techniques to improve diagnostic accuracy and practical application in clinical settings. …”
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