The urgency of the androgenic screening for men who underwent preventive medical examination for prostate diseases detection by A. A. Kamalov, M. Ye. Chaly, R. P. Vasilevsky

“…The bad influence of the androgenic insufficiency for men defines the need for obligatory androgenic screening of more than 50 years old patients. Testosterone level was examined. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

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Screening OSA in Chinese Smart Device Consumers: A Real-World Arrhythmia-Related Study by Chen Y, Zhang H, Li J, Xu P, Guo Y, Xie L

“…Our previous study validated an algorithm-based photoplethysmography (PPG) smartwatch for OSA risk detection.Objective: This study aimed to characterize OSA features and assess its association with arrhythmia risk among smart wearable device (SWD) consumers in China in a real-world setting.Methods: Between December 15, 2019, and January 31, 2022, SWD consumers across China were screened for OSA risk using HUAWEI devices. …”
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Predicting the risk of lean non-alcoholic fatty liver disease based on interpretable machine models in a Chinese T2DM population by Shixue Bao, Qiankai Jin, Tieqiao Wang, Yushan Mao, Guoqing Huang

“…Feature screening was performed using the Boruta algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO). …”
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Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model by Lufeng Chen, Qingquan Chen, Zhimin Huang, Ling Yao, Jiajing Zhuang, Haibin Lu, Yifu Zeng, Jimin Fan, Ailing Song, Yixiang Zhang

“…A total of eight significant predictors finally identified by the LASSO algorithm was incorporated into prediction models. …”
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Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

“…Features were filtered using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). …”
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Efficient text-to-video retrieval via multi-modal multi-tagger derived pre-screening by Yingjia Xu, Mengxia Wu, Zixin Guo, Min Cao, Mang Ye, Jorma Laaksonen

“…In this work, we present a plug-and-play multi-modal multi-tagger-driven pre-screening framework, which pre-screens a substantial number of videos before applying any TVR algorithms, thereby efficiently reducing the search space of videos. …”
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Preterm preeclampsia screening and prevention: a comprehensive approach to implementation in a real-world setting by Stefania Ronzoni, Shamim Rashid, Aimee Santoro, Elad Mei-Dan, Jon Barrett, Nanette Okun, Tianhua Huang

“…Abstract Background Preeclampsia significantly impacts maternal and perinatal health. Early screening using advanced models and primary prevention with low-dose acetylsalicylic acid for high-risk populations is crucial to reduce the disease’s incidence. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them. …”
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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

“…By comparing the enrichment results across the five datasets, we found that the cell-killing signaling pathway was consistently present in the enriched signaling pathways of all datasets, suggesting that this pathway may play a crucial role in the pathogenesis of MDD. The random forest algorithm (AUC = 0.788) was selected as the optimal algorithm from 113 machine learning algorithms, leading to the development of a robust and predictive MDD algorithm, highlighting the important role of NPL in MDD. …”
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Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…Five machine learning algorithms were compared, and the best-performing model was selected based on the area under the receiver operating characteristic curve. …”
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A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…<b>Background/Objectives:</b> The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. …”
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Attachment beyond the screen: The influences of demographic factors and parasocial relationships on social media use in Qatar. by Ruining Jin, Tam-Tri Le

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…A classification prediction model for the haemoglobin concentration after kidney transplantation was constructed. …”
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Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model by Wanru Zhao, Ziteng Liu, Rui Zhang, Mai Lu, Wenhui Zhao

“…Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. …”
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Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India by Ramachandran Rajalakshmi, Thyparambil Aravindakshan PramodKumar, Ashis Kumar Dhara, Geetha Kumar, Naziya Gulnaaz, Shramana Dey, Sourav Basak, B Uma Shankar, Raka Goswami, Raja Mohammed, Suchetha Manikandan, Sushmita Mitra, Harsimran Thethi, Saravanan Jebarani, Sinnakaruppan Mathavan, Tamilselvi Sarveswaran, Ranjit Mohan Anjana, Viswanathan Mohan, Sambuddha Ghosh, Tushar Kanti Bera, Rajiv Raman

“…Our work is expected to mark a significant stride in DR detection and management, promising a more efficient and scalable solution for tackling this global health challenge.…”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). …”
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Nomogram model using serum Club cell secretory protein 16 to predict prognosis and acute exacerbation in patients with idiopathic pulmonary fibrosis by Yaqiong Tian, Xuan Zhou, Mi Tian, Lijun Ren, Ruyi Zou, Hanyi Jiang, Miaomiao Xie, Mei Huang, Jingjing Ding, Yin Liu, Jingyu Chen, Min Cao, Hourong Cai

“…COX regression and LASSO algorithm were used to screen featured characteristics. …”
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Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma by Ting Ji, Ting Ji, Juanli Jiang, Juanli Jiang, Xin Wang, Xin Wang, Kai Yang, Kai Yang, Shaojin Wang, Shaojin Wang, Bin Pan, Bin Pan

“…Using the GeneCards ferroptosis gene set (1515 genes), ferroptosis-related differentially expressed genes in macrophages were screened. Eight machine learning algorithms (LASSO, SVM, XGBoost, etc.) were leveraged to identify prognostic genes and build a Cox regression risk model. …”
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Developing an HIV-specific falls risk prediction model with a novel clinical index: a systematic review and meta-analysis method by Sam Chidi Ibeneme, Eunice Odoh, Nweke Martins, Georgian Chiaka Ibeneme

“…Abstract Background Falls are a common problem experienced by people living with HIV yet predictive models specific to this population remain underdeveloped. …”
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