Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors by Akira Minamisawa, Shogo Okada, Ken Inoue, Mami Noguchi

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Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…Weighted cognitive avoidance particle swarm optimization for the XGBoost (WCAPSO-XGB) model has been proposed for credit score classification. The structure of the XGBoost (XGB) model is determined by the hyperparameters, which must be initialized prior to model evaluation. …”
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Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis by Jaehyuk Lee, Eunchan Kim

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Bioinformatics analysis reveals key mechanisms of oligodendrocytes and oligodendrocyte precursor cells regulation in spinal cord Injury by Xi Yue, Xunling Chen, Yang Zang, Jinliang Wu, Guanhao Chen, Hongyu Tan, Kerong Yang

“…This model includes three types: Low ODC/OPC Score Classification (LOSC), Median ODC/OPC Score Classification (MOSC), and High ODC/OPC Score Classification (HOSC). …”
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BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images by Michael John Fanous, Christopher Michael Seybold, Hanlong Chen, Nir Pillar, Aydogan Ozcan

“…BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.…”
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Behavior classification: Introducing machine learning approaches for classification of sign-tracking, goal-tracking and beyond. by Camille Godin, Frédéric Huppé-Gourgues

“…To address this issue, we explored two approaches to PavCA Index score classification: the k-Means classification and the derivative method. …”
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Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm by Hongyan Wang

“…The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. …”
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Enhanced Isolation Forest-Based Algorithm for Unsupervised Anomaly Detection in Lidar SLAM Localization by Guoqing Geng, Peining Wang, Liqin Sun, Han Wen

“…Finally, to further increase the model’s reliability, we employed an adaptive OTSU (Otsu’s method) algorithm for automatic score classification. Experimental results show that our proposed model can effectively detect positioning anomalies by determining variable thresholds in four scenarios of the KITTI dataset. …”
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RISK STRATIFICATION PROTOCOL FOR PERFORMING TOTAL KNEE ARTHROPLASTY by Fabrício Bolpato Loures, Guilherme de Mattos Queiroz, Danielle Lopes Rosa, Guilherme Morgado Runco, Liszt Palmeira de Oliveira, Vinícius Schott Gameiro

“…Method: between January 2020 and December 2021, 270 TKA were performed, following a clinical protocol: age up to 75 years, body mass index up to 35 kg/m2, ASA score classification I or II, non-smoker, without history of ischemic disease (coronary or cerebral), creatinine clearance greater than 60 mL/min, hemoglobin greater than 12 g/dL and osteoarthritis with deformity treatable with primary prosthesis (fixed or rotating base). …”
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Ulcerative Severity Estimation Based on Advanced CNN–Transformer Hybrid Models by Boying Nie, Gaofeng Zhang

“…The best model is compared against pure CNN and transformer baselines by evaluating performance metrics, including quadratically weighted kappa (QWK) and macro <i>F</i>1, for full Mayo score classification, and kappa and <i>F</i>1 scores for remission classification. …”
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Accelerometer based independent and combined associations of physical activity and sedentary time on physical fitness in preschool children: a cross-sectional study by Chunyi Fang, Longkai Li, Zhenhua Jin, Changshuang He, Feng Liang, Xiangming Ye, Yaofei Lu, Minghui Quan

“…The high active/low sedentary Group had the highest odds of higher handgrip strength, musculoskeletal fitness, and fitness score. Classification as high active/low sedentary increased the probability of a high fitness score by 30% (β, 0.30; 95% CI, 0.07, 0.54). …”
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Comparing Surgical Approaches for Thumb Polydactyly Reconstruction in Wassel Type IV: K-wire Fixation Versus Non-K-wire Fixation by Chi Peng, MD, Che-Hsiung Lee, MD, Shih-Heng Chen, MD, Ren-Wen Huang, MD, Cheng-Hung Lin, MD, Yu-Te Lin, MD, MSc, FACS, Chung-Chen Hsu, MD

“…Outcomes were evaluated using Tada score classification, MPJ deviation angle, and range of motion. …”
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Outcomes of surgical management for temporomandibular joint ankylosis and pseudoankylosis: a retrospective report of 26 cases by Kristin Kocsis, Stephanie Goldschmidt, Graham Paul Thatcher, Charles Lothamer, Lisa Alexandra Mestrinho, Lisa Alexandra Mestrinho

“…Surgical treatment outcomes were categorized with a proposed improvement score classification system based on the percent range of motion (ROM) improvement, requirement for revision surgery, and presence of transiente or permanent complications. …”
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Assessment of the Diagnostic Performance of MUAC in Malnutrition Screening and Its Correlation with Other Anthropometric Indicators in Healthy Children and Adolescents by Hatice Esra Durukan, Burçe Emine Dörtkardeşler, Merve Tosyalı, Şule Gökçe, Nuri Zafer Kurugöl, Feyza Koç

“…Results: According to the WHO BMI z-score classification, 6 (0.7%) of the children were defined as having severe undernutrition, 43 (4.7%) as moderate undernutrition, 146 (16.1%) as mild undernutrition, 486 (53.6%) as normal, 142 (15.7%) as overweight, and 83 (9.2%) as obese. …”
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Exploring predictors of insomnia severity in shift workers using machine learning model by Hyewon Yeo, Hyeyeon Jang, Nambeom Kim, Sehyun Jeon, Yunjee Hwang, Chang-Ki Kang, Seog Ju Kim, Seog Ju Kim

“…The prediction model demonstrated good performance with high accuracy and specificity overall despite a limited F1 score (classification effectiveness) and recall (sensitivity). …”
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Risk Factors for Delayed Diagnosis of Pyogenic Spondylitis: A Cross-Sectional Study with Prospective Case Series by Tomoya Sato, Katsuhisa Yamada, Keigo Yasui, Junichiro Okumura, Masahiro Kanayama, Ryota Hyakkan, Hiroyuki Hasebe, Yuichi Hasegawa, Hiroshi Nakayama, Tsutomu Endo, Daisuke Ukeba, Hiroyuki Tachi, Toshiya Chubachi, Hideki Sudo, Masahiko Takahata, Manabu Ito, Norimasa Iwasaki

“…Univariate analysis of risk factors for delayed diagnosis revealed that the significant risk factors included advanced age (p=0.03), low white blood cell count (p<0.01), low C-reactive protein level (p<0.05), and semi-rigid spinal level, based on the spinal instability neoplastic score classification (p=0.05). Multivariate analysis for delayed diagnosis showed that the location at the semi-rigid spinal level was a significant risk factor (p=0.02). …”
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Association of metabolic dysfunction-associated steatotic liver disease and steatosis-associated fibrosis estimator with subclinical coronary atherosclerosis: observation cohort st... by Joonho Jeong, Seungbong Han, Gyung-Min Park, Sangwoo Park, Young-Jee Jeon, Soyeoun Lim, Woon Jung Kwon, Seong Hoon Choi, Neung Hwa Park

“…Subtypes of SLD had significant, yet different strengths of associations with subclinical coronary atherosclerosis. SAFE score classification effectively stratified the distinct associations with subclinical atherosclerosis in subjects with MASLD.…”
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Objective dairy cow mobility analysis and scoring system using computer vision–based keypoint detection technique from top-view 2-dimensional videos by Shogo Higaki, Guilherme L. Menezes, Rafael E.P. Ferreira, Ariana Negreiro, Victor E. Cabrera, João R.R. Dórea

“…Subsequently, a 3-level mobility score classification model (score 0, 1, and 2 + 3) was developed using the random forest algorithm, based on the extracted mobility variables. …”
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Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production by Feiyang Ou, Julius Suherman, Chao Zhang, Henrik Wang, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…XGBoost, a gradient descent-based tree algorithm, outperforms the commonly used Feedforward Neural Networks (FNN) in terms of training speed and resource utilization for binary-classifications, as well the performance criterion in ROC-AUC score (classification), Median Absolute Error (regression) and R2 value. …”
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Systematic screening for atrial fibrillation with non-invasive devices: a systematic review and meta-analysisResearch in context by Ali Wahab, Ramesh Nadarajah, Harriet Larvin, Maryum Farooq, Keerthenan Raveendra, Mohammad Haris, Umbreen Nadeem, Tobin Joseph, Asad Bhatty, Chris Wilkinson, Kamlesh Khunti, Rajesh Vedanthan, A John Camm, Emma Svennberg, Gregory YH. Lip, Ben Freedman, Jianhua Wu, Chris P. Gale

“…The use of age and NT-proBNP (IR 4.36%, 95% CI 3.77–5.08) or AF risk score classification (4.79%, 95% CI 3.62–6.29) led to higher new AF diagnosis yields than age alone (0.93%, 95% CI 0.28–2.99). …”
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