Predicting depressive symptoms through social support: a machine learning approach in military populations by Kun-Huang Chen, Pao-Lung Chiu, Ming-Hsuan Chen

“…Five ML classifiers, Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, and k-Nearest Neighbors, were applied to predict depressive symptoms, with model performance evaluated across full and subgroup samples.Results: The Random Forest model achieved the highest area under the precision-recall curve (AUPRC) at 96.3% and consistently outperformed other classifiers across a range of evaluation metrics. …”
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Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency by Leszek Sliwko

“…Task constraint operators are compacted, pre-processed with one-hot encoding, and used as features in a training dataset. Various ML classifiers, including Artificial Neural Networks, K-Nearest Neighbours, Decision Trees, Naive Bayes, Ridge Regression, Adaptive Boosting, and Bagging, are fine-tuned and assessed for accuracy and F1-scores. …”
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Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy by Miguel Angel Bergero, Pablo Martínez, Patricio Modina, Ricardo Hosman, Wenceslao Villamil, Romina Gudiño, Carlos David, Lucas Costa

“…A retrospective cohort of 1024 (476 BCR+ and 548 BCR−) patients was analyzed, using a balanced dataset of 25 clinical and pathological variables. Five ML classifiers were evaluated, with XGBoost emerging as the best-performing model, achieving 84% accuracy and an AUC of 0.91. …”
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Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes by Johannes Miedema, Beat Lutz, Susanne Gerber, Irina Kovlyagina, Hristo Todorov

“…In contrast to data-driven clustering, the performance of ML classifiers remained unaffected by sample size and modifications to the conditioning protocol. …”
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Understanding drug abstinence self efficacy through statistical analysis, machine learning and explainable AI by Priti Rekha Das, Rita Rani Talukdar, Chandan Jyoti Kumar

“…Abstract Objective This study explores the socio-demographic and psychological factors influencing Drug Abstinence Self-Efficacy (DASE) through a combined Statistical and Machine Learning (ML) framework, aiming to enhance understanding and improve intervention strategies for individuals with substance use disorders. …”
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Enhancing blockchain transaction classification with ensemble learning approaches by Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Rourab Paul, Ajit Kumar Nayak, Subrata Chowdhury, Ramya Govindaraj, J Shreyas

“…This research aims to develop a machine learning (ML) based model for classifying blockchain transactions into risky or non-risky ones. …”
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A deep learning model to predict glioma recurrence using integrated genomic and clinical data by Jessica A. Patricoski-Chavez, Seema Nagpal, Ritambhara Singh, Jeremy L. Warner, Ece D. Gamsiz Uzun

“…Conclusions Our results demonstrate the potential of multimodal DL classifiers for predicting early glioma recurrence. …”
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Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases by Roshan Kumar, V Srirama, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga

“…The KBest feature selection method is used initially to get the best fifteen features from the dataset. The random forest classifier performed well with the best accuracy of 96% compared to other classifiers. …”
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Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder by Tyler N. Meyer, Olga Andreeva, Roger D. Weiss, Wei Ding, Iris Shen, Changning Wang, Ping Chen, Tewodros Mulugeta Dagnew

“…These were used to train and test ML classifiers, including Support Vector Machine (SVM), XGBoost, and Random Forest, under leave-one-out cross-validation. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…Hybrid models are developed based on Transformer models: Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), and Swin Transformer are used to extract deep features from images, Principal Component Analysis (PCA) is used to reduce the complexity of extracted features, and Machine Learning (ML) models are used as classifiers to enhance performance. …”
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NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides by Chengzhi Xie, Yijie Wei, Xinwei Luo, Huan Yang, Hongyan Lai, Fuying Dao, Juan Feng, Hao Lv

“…The first stage generates preliminary predictions using four distinct encoding strategies and ML classifiers, while the second stage employs a multi-branch residual network (ResNeXt) to refine prediction outputs. …”
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Prediction and Correction of Software Defects in Message-Passing Interfaces Using a Static Analysis Tool and Machine Learning by Norah Abdullah Al-Johany, Fathy Elbouraey Eassa, Sanaa Abdullah Sharaf, Amin Y. Noaman, Asaad Ahmed

“…Results show the NB classifiers have high accuracy, precision, and recall, which are about 1.…”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as M. morganii and 26 as M. sibonii. Results: The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). …”
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Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter by Bagus Setya Rintyarna

“…Ada dua pendekatan yang umum digunakan dalam teknik sentiment analysis yaitu pendekatan berbasis machine learning (ML) dan pendekatan berbasis sentiment lexicon (SL). …”
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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery by E. Z. Golukhova, M. A. Keren, T. V. Zavalikhina, N. I. Bulaeva, D. S. Akatov, I. Yu. Sigaev, K. B. Yakhyaeva, D. A. Kolesnikov

“…Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. …”
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Machine learning to predict bacteriuria in the emergency department by Johnathan M. Sheele, Ronna L. Campbell, Derick D. Jones

“…We used a logistic regression classifier, k-nearest neighbors, random forest classifier, extreme gradient boosting (XGBoost), and a deep neural network to determine how well they predicted 3 urine culture outcomes: (1) no microbial growth vs. any microbial growth, including mixed flora; (2) ≥10,000 colony-forming units per milliliter (CFU/mL) for ≥1 organism vs. < 10,000 CFU/mL for all organisms; and (3) ≥100,000 CFU/mL for ≥1 organism vs. < 100,000 CFU/mL for all organisms. …”
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Relationship between Mildly Elevated NT-proBNP Levels and Heart Failure Stages in the Elderly by Rana A. Hallak, Ahmed Alakedi, Mona Kardus, Ebtihal Al Amoudi, Ameerah Munassar, Mary Jaison, Hussein Zayed, Mohammed Jabal, Arwa Al Shatiri, Fahad Al Mubarak, Adel Saad, Rama Sarraj, Jawa Sarraj

“…Previous data indicates that NT-proBNP levels above 450.0 pg/mL in patients aged 75 and older may indicate heart failure. …”
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A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data by J.L. Amorim, I.M. Bensenor, A.P. Alencar, A.C. Pereira, A.C. Goulart, P.A. Lotufo, I.S. Santos

“…Feature importance was determined by SHapley Additive exPlanations (SHAP) values. The best performer ML algorithm was the XGBoost Classifier (accuracy: 94.8%). …”
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Comparative Analysis of Rheumatoid Factor Levels by Immune Turbidimetry and Latex Agglutination Assays among Anti-Cyclic Citrullinated Peptide-Positive Rheumatoid Arthritis Patient... by Bineeta Kashyap, Rituparna Saha, Krishna Sarkar, Narendra Pal Singh

“…Latex agglutination test for RF using RHELAX-RF test kit yielded RF values ≥10 IU/ml in 31 (62%) patients, while the Immunoturbidimetric test: SPECTRUM RF Test Kit imparted RF titers ranging between 2.4 and 53.76 IU/ml, with a median RF titer of 22.22 IU/ml. …”
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Leveraging hybrid model for accurate sentiment analysis of Twitter data by Naga Surekha Jonnala, A. V. S. Ram Teja, S. Raja Rajeswari, Shaik Jakeer, Allamsetty Dheeraj, Shonak Bansal, Krishna Prakash, Shashank Singh, Mohammad Rashed Iqbal Faruque, K. S. Al-mugren

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