Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…For nomogram construction, we utilized an ensemble machine learning framework, combining Boruta algorithm-based feature selection with Random Forest (RF) and XGBoost analyses to determine key predictive parameters.ResultsThroughout the median follow-up duration of 84 months, we documented 1,500 incident CVD cases, comprising 1,148 cardiac events and 488 cerebrovascular events. …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…This is completely evaluated against other deep learning algorithms.…”
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Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance by Eslam Abdelhakim Seyam

“…The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. …”
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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang, Yuxin Huo

“…The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Feature selection was performed using univariate Cox regression (p ≤ 0.05), and four ML algorithms—Random Survival Forest (RSF), Gradient Boosting Machine (GBM), CoxBoost, and XGBoost—were applied to develop recurrence prediction models. …”
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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

“…Methods: Consensus clustering identified VRG-associated subtypes. We developed a machine learning framework integrating 12 algorithms to establish a consistent VM-related signature (VRG_score). …”
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Research on Bearing Fault Diagnosis Method Based on MESO-TCN by Ruibin Gao, Jing Zhu, Yifan Wu, Kaiwen Xiao, Yang Shen

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DETECTING URBAN SLUMS IN DKI JAKARTA: A KOTAKU DATA APPROACH WITH ENSEMBLE METHODS by Muhammad Muawwad MS, Rani Nooraeni, Ananda Galuh Intan Prasetya

“…This modeling will look for patterns or structures from the data that has been provided so that the detection results become more objective. …”
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Privacy-Aware Detection for Large Language Models Using a Hybrid BiLSTM-HMM Approach by Maryam Abbasalizadeh, Sashank Narain

“…Our approach employs the Predefined and Sensitive Labeling (PSL) technique, a generative labeling approach that extracts meaningful patterns from data. These patterns are then used to train a BiLSTM model capable of proactively identifying sensitive information in real-time user interactions with LLMs. …”
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Improved Splitting-Integrating Methods for Image Geometric Transformations: Error Analysis and Applications by Hung-Tsai Huang, Zi-Cai Li, Yimin Wei, Ching Yee Suen

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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

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CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation by Juan Juan Chen, Zhang Ze Lu, Yu Xin Jing, Xing Mei Nong, Yi Qin, Jin Yang Huang, Na Lin, Jie Wei

“…Immune cell profiling highlighted distinct infiltration patterns, with severe cases showing elevated naïve B cells and M0 macrophages versus activated NK cells and M1 macrophages in mild cases. …”
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Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station by Luis Hernán Ochoa Gutiérrez, Luis F Niño, Carlos A. Vargas

“…We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS's National Seismological Network at a three-component station located near Bogota, Colombia. …”
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CTSS in the tumor microenvironment links immune escape and immunotherapy sensitivity in kidney renal clear cell carcinoma by Hanjing Zhou, Jun Ying, Xuchun Xu, Jian Huang

“…Employing advanced machine learning (ML) algorithms, we identified Cathepsin S (CTSS) as the most pivotal tumor suppressor, with elevated CTSS expression consistently predicting improved survival across multiple independent cohorts. …”
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The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

“…We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…The highlighted gap can be addressed by machine learning (ML), as it has been effectively used in the past to support the analysis and knowledge discovery of communication systems’ traffic data through identification of intricate and hidden patterns. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. …”
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Introduction to Computational Creativity by Anna Longo, ChatGPT

“…By deconstructing the cognitive processes involved in human creativity, researchers can design algorithms that simulate these processes. This involves machine learning, neural networks, evolutionary algorithms, and other AI techniques that enable computers to recognize patterns, generate new ideas, and refine them through iterative processes.  …”
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The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

“…Several types of features are extracted from both datasets. Machine learning algorithms using different feature sets achieved nearly 90-95% for the CS group and 70-85% accuracy for the PSF. …”
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