Efficacy and safety of Shuxuening injection in intracerebral hemorrhage: a systematic review and meta-analysis by Wenting Song, Yaoyuan Liu, Yaoyuan Liu, Chaofan Kang, Yazi Zhang, Xing Yan, Xinyao Jin, Yuetong Wang, Fengwen Yang, Wentai Pang

“…The methodological quality of the included studies was assessed using the revised Cochrane Risk of Bias tool (ROB 2.0). For binary variables, risk ratios (RR) were calculated, while for continuous variables, mean differences (MD) or standardized mean differences (SMD) were calculated, based on 95% confidence intervals (CI).ResultsA total of 29 trials involving 3,012 participants were included. …”
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CRF1 receptor antagonists in congenital adrenal hyperplasia: A systematic review and meta-analysis of phase 2 open-label and phase 3 clinical trials by Mir wajid Majeed, Emma Finnegan, Mariano Gallo Ruelas, Marco Quirós, Marina Barbosa da Silva, Issa Salha, Catalina Herrán-Fonseca, Helen Michaela de Oliveira, Melissa Chacón Quirós, Raheel Ahmed, Zainab Humayun, Tajamul Hussain Shah, Mohammad Ashraf Ganie

“…Binary data was pooled from Phase 3 clinical trials. …”
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Value of a BRAFV600E and lymphocyte subset-based nomogram for discriminating benign lesions from papillary thyroid carcinoma in C-TIRADS 3 and higher nodules by Wenran Zhang, Simei Zeng, Jiaqing Dou, Chenfan Yu

“…This study established and validated a nomogram model to quantitatively predict the malignant risk of papillary thyroid carcinoma in thyroid nodules classified as C-TIRADS category 3 or higher, providing a reference for precise diagnosis and treatment of these moderately or highly suspicious nodules.MethodsThis retrospective study analyzed 210 patients with thyroid nodules (C-TIRADS ≥3), stratified by fine-needle aspiration biopsy (FNAB) results into benign and PTC groups. …”
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Effect of Combination of Point-of-Care C-Reactive Protein Testing and General Practitioner Education and Long-Term Effect of Education on Reducing Antibiotic Prescribing for Childr... by Zane Likopa, Anda Kivite-Urtane, Ieva Strele, Jana Pavare

“…Methods: This was a randomized controlled intervention study with randomization at the GP practice level. …”
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Beyond p-values: a cross-sectional umbrella review of chemotherapy-induced peripheral neuropathy treatments by Alice L. Ye, Salahadin Abdi

“…We focused our analysis on the three most researched treatment options: oral drugs, exercise, and acupuncture. …”
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Development and Evaluation of Effectiveness of a Universal Behavior Change Communication (UBCC) Model by Abhishek Mukherjee, Avantika Gupta, Geeta Patel, JK Kosambiya

“…Methodology A multiphase mixed-method study from June 2022 to July 2023. Phase I and III were conducted in urban field practice areas, where two Galis each were selected using simple random sampling. …”
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Early detection of Alzheimer’s disease in structural and functional MRI by Rudrani Maity, Vellupillai Mariappan Raja Sankari, Umapathy Snekhalatha, Umapathy Snekhalatha, Shubashini Velu, Tahani Jaser Alahmadi, Zaid Ali Alhababi, Hend Khalid Alkahtani

“…Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer’s disease, comparing their performance against traditional classifiers.MethodOpenNeuro and Harvard’s Data verse provides Alzheimer’s coronal functional MRI data. …”
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MEFET-Based CAM/TCAM for Memory-Augmented Neural Networks by Sai Sanjeet, Jonathan Bird, Bibhu Datta Sahoo

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Prevalence and determinants of vulnerability among Sundarbans mangrove forest resource-dependent communities in cyclone-prone southwestern coastal districts of Bangladesh by Md. Tanvir Hossain, Tunvir Ahamed Shohel, Md. Nasif Ahsan, Md. Nazrul Islam

“…Data were collected from 782 SMFRDCs in three Upazila (sub-district) of selected coastal districts using a structured interview schedule (SIS) and following a multistage stratified random sampling approach. …”
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Identification of direct and indirect drivers of land use and land cover changes from agriculture to Eucalyptus plantation using the DPSIR framework in Sinan and Mecha Districts of... by Kassa Chanie Wubetie, Asabeneh Alemayehu, Engidayehu Melaku

“…We used purposive and simple random sampling to select study areas and households. …”
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Adaptation of Red Upland Rice Farmers to Climate Change in Semanu District, Gunungkidul Regency by Rahayu Lestari, Wijaya Oki, Cahya Addin Dinar

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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). …”
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Optimizing Cardiovascular Risk Assessment with a Soft Voting Classifier Ensemble by Ammar Oad, Zulfikar Ahmed Maher, Imtiaz Hussain Koondhar, Karishima Kumari, Hammad Bacha

“…The proposed ensemble soft voting classifier employs an ensemble of seven machine learning algorithms to provide binary classification, the Naïve Bayes K Nearest Neighbor SVM Kernel Decision Tree Random Forest Logistic Regression and Support Vector Classifier. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. …”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

“…Results: Internal validation achieved exceptional predictive accuracy, with the area under the receiver operating characteristic curve (ROC-AUC) reaching 0.94 and 0.98 on the binary task of predicting kidney failure for decision tree and random forest, respectively. …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

“…The pivotal solution was implementation of ML techniques which enable processing of seemingly unrelated information concerning pressures in the catchment. Decision Tree, Random Forest, KNN, Support Vector Machine, Multinomial Naive Bayes, XGBoost models have been tested and the results indicated most suitable techniques. …”
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A feature selection and scoring scheme for dimensionality reduction in a machine learning task by PHILEMON UTEN EMMOH, christopher ifeanyi Eke, Timothy Moses

“…The experimental results of the proposed technique on lung cancer dataset shows that logistic regression, decision tree, adaboost, gradient boost and random forest produced a predictive accuracy of 0.919%, 0.935%, 0.919%, 0.935% and 0.935% respectively, and that of happiness classification dataset produced a predictive accuracy of 0.758%, 0.689%, 0.724%, 0.655% and 0.689% on random forest, k-nearest neighbor, decision tree, gradient boost and cat boost respectively, which outperformed the existing techniques. …”
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Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches by T. Anitha Kumari, Sanket Mishra

“…This paper introduces Tachyon, a combination of various statistical and tree-based Artificial Intelligence (AI) techniques, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Auto-Regressive Transformers (BART), Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Decision Tree (DT), and a top k stack ensemble to distinguish between normal and malicious attacks in a binary classification setting. …”
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Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning by Amr Eledkawy, Taher Hamza, Sara El-Metwally

“…A majority vote feature selection process is employed by combining six feature selectors: Information Value, Chi-Square, Random Forest Feature Importance, Extra Tree Feature Importance, Recursive Feature Elimination, and L1 Regularization. …”
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Frobenius deep feature fusion architecture to detect diabetic retinopathy by C. Priyadharsini, Y. Asnath Victy Phamila

“…The proposed approach delves into various phases- data collection and data pre-processing, feature extraction from VGG16 and Densenet201, feature selection using Random Forest, feature fusion using Frobenius norm, and classification using stacked ensembling of XGBoost classifier and ExtraTreeClassifier with SVC as meta-learner. …”
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