Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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Carbon stock dynamics of forest to oil palm plantation conversion for ecosystem rehabilitation planning by D. Frianto, E. Sutrisno, A. Wahyudi, E. Novriyanti, W.C. Adinugroho, A.S. Yunianto, H. Kurniawan, H. Khotimah, A. Windyoningrum, I.W.S. Dharmawan, H.L. Tata, S. Suharti, H.H. Rachmat, E.M. Lim

“…BACKGROUND AND OBJECTIVES: Efforts to enhance carbon stocks and boost carbon absorption potential are essential for climate change mitigation. …”
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Unveiling Cuproptosis-Driven Molecular Clusters and Immune Dysregulation in Ankylosing Spondylitis by Wei B, Wang S, Li S, Gu Q, Yue Q, Tang Z, Zhang J, Liu W

“…The eXtreme Gradient Boosting (XGB) model showed the highest predictive accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.725, and 5-gene prediction models were established. …”
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Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos by Mingliang Zuo, Xiaoyu Chen, Li Sui

“…To evaluate classification performance, several classical machine learning algorithms were employed: ssupport vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), naive Bayes, decision Tree, AdaBoost, and a voting classifier. The study systematically compared the classification performance of PSD and CSP features across these algorithms, providing a comprehensive analysis of their effectiveness in distinguishing EEG signals in response to 2D and 3D VR stimuli. …”
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The clinical prediction model to distinguish between colonization and infection by Klebsiella pneumoniae by Xiaoyu Zhang, Xifan Zhang, Deng Zhang, Jing Xu, Jingping Zhang, Xin Zhang

“…Six predictive models were constructed using 15 key influencing factors, including Classification and Regression Trees (CART), C5.0, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), Random Forest (RF), and Nomogram. …”
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A two-stage model of the fuzzy analytic hierarchy process and the fuzzy synthetic evaluation technique to prioritize sustainable sanitation services under uncertainty by Shaher Zyoud, Siwar M. Omair, Susan A. Jarrad

“…Introducing this model into relevant bodies’ sanitation management practices and planning strategies holds the potential to significantly boost sustainable sanitation services as well as the performance of water and wastewater utilities. …”
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Associations between milk infrared-predicted plasma biomarkers of stress resilience and fertility in dairy cattle: Insights for enhancing breeding programs and herd management by Alessio Cecchinato, Hugo Toledo-Alvarado, Lucio Flavio Macedo Mota, Vittoria Bisutti, Erminio Trevisi, Riccardo Negrini, Sara Pegolo, Stefano Schiavon, Luigi Gallo, Giovanni Bittante, Diana Giannuzzi

“…The blood metabolites (15 blood biomarkers related to hepatic damage and function, oxidative stress, inflammation, and innate immunity) were predicted using milk Fourier-transform mid-infrared (MIR) spectroscopy. A gradient boosting machine approach with leave-one-batch-out cross-validation (R2 range from 0.45 to 0.82) was implemented to an independent calibration database of 1,367 lactating cows reared in 5 herds. …”
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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Creating a harmonized dataset by pooling repeated cross-sectional surveys to model trends in CVD risk is challenging due to changes in aspects such as survey content, question wording, inclusion of boost samples, weighting, measuring equipment, and guidelines for data protection. …”
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Production of Biogas from Bio-Degradable Municipal Solid Waste – A Way of Resource Recovery. by Atatiru, Faiza

“…A mixture of MSW (80%) and cow dung (20%) of the total reactor volume was then fed into the bio digester and the slurry PH values 12.6, 8.9, 4.9, 5.3 7.8 4.0, 4.8, 5.8, 5.9, 5.9, 5.9, 5.8 and 5.8 were recorded with varying masses of the gas storage unit in the first 13 days without introduction of any bio boost into the mixture. This clearly showed that the MSW has the potential to generate biogas under controlled conditions in Kabale Municipality.…”
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A Model for Organizational Preparedness of SMEs During COVID-19 Pandemic in Kigezi Sub-region in South Western Uganda. by Tamwesigire, Caleb, Nafiu, Lukman Abiodun

“…There is also need for government/NGO interventions in the area of subsidy for SME owners/managers during the pandemic to boost their businesses. Keywords: Organizational Features, Business Disaster, Decision makers, Pandemic, COVID…”
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Joint Education Provision: A Relief or Challenge to Quality Education Services in Uganda. “A Study in Buganda Region”. by Sempungu, Godfrey

“…The study, in general, investigated the problems that affect the Ugandan school system, reviewed the government's position in lower levels of academia, and gave policy recommendations and suggestions for boosting school performance in light of the current performance. …”
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Comparative analysis of clinical efficacy of unilateral biportal endoscopic and open transforaminal lumbar interbody fusion in the treatment of lumbar degenerative by Tao Ma, Xiaoshuang Tu, Junyang Li, Yongcun Geng, Yongcun Geng, Jingwei Wu, Senlin Chen, Dengming Yan, Dengming Yan, Ming Jiang, Ming Jiang, Gongming Gao, Luming Nong

“…CT scans at 3 months postoperatively were used to observe intervertebral fusion, including bridging trabeculae, endplate cysts, and screw loosening. MRI at 1 year postoperatively was used to manually trace the cross-sectional area of the paraspinal muscles to compare muscle atrophy.ResultsA total of 150 patients were included in the study, with 71 patients in the ULIF group and 79 patients in the TLIF group. …”
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The success of reforestation after experimental strip-shelterwood felling in the pine stands of the Eastern Polissia of Ukraine by Anatolyi Zhezhkun

“…After felling carried out in the autumn or spring, natural regeneration was promoted by loosening the soil with disk cultivators or by making furrows with ploughs. …”
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Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI by Dong X, Meng J, Xing J, Jia S, Li X, Wu S

“…Future research should expand to multicentric studies and include genomic data to boost the nomogram’s generalizability and precision.Keywords: young onset breast cancer, clinical-radiomics nomogram, axillary lymph node metastasis, dynamic contrast-enhanced MRI…”
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Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring by Anisa Nur Azizah, Misbachul Falach Asy'ari, Ifnu Wisma Dwi Prastya, Diana Purwitasari

“…Then, the experiments investigate our augmentation process using classifiers of Random Forest, Naïve Bayes, and boosting-based methods like XGBoost and ADABoost, which resulted in an average accuracy increase of 0.63. …”
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Identification of multiple complications as independent risk factors associated with 1-, 3-, and 5-year mortality in hepatitis B-associated cirrhosis patients by Duo Shen, Ling Sha, Ling Yang, Xuefeng Gu

“…Eight machine learning techniques were employed to construct predictive models, including C5.0, linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), k-nearest neighbour (KNN), gradient boosting decision tree (GBDT), support vector machine (SVM), generalised linear model (GLM) and naive Bayes (NB), utilising variables such as medical history, demographics, clinical signs, and laboratory test results. …”
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Association Between Metabolic Syndrome and Cardiac Autonomic Nervous Function and Cardiorespiratory Fitness in Older Adults: A Retrospective Observational Study with Propensity Sco... by Cui N, Li Q, Cheng J, Xing T, Shi P, Wang Y, Luo M, Dun Y, Liu S

“…It is recommended that they boost physical activity and closely monitor heart rate and blood pressure during exercise to mitigate exercise-related risks.Keywords: aging population health, heart rate recovery, systolic blood pressure recovery, geriatric cardiology, autonomic regulation, fitness evaluation…”
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Examining the Use of Machine Learning Algorithms to Enhance the Pediatric Triaging Approach by Aljubran HJ, Aljubran MJ, AlAwami AM, Aljubran MJ, Alkhalifah MA, Alkhalifah MM, Alkhalifah AS, Alabdullah TS

“…Notably, ensemble algorithms surpassed other models in all evaluation metrics, with CatBoost achieving an F-1 score of 90%. Importantly, the model never misclassified an urgent patient as nonurgent or vice versa.Conclusion: The study successfully created a machine learning model to classify pediatric emergency department patients into three urgency levels. …”
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Elevating larval source management as a key strategy for controlling malaria and other vector-borne diseases in Africa by Fredros Okumu, Sarah J. Moore, Prashanth Selvaraj, Arnon Houri Yafin, Elijah O. Juma, GloriaSalome G. Shirima, Silas Majambere, Andy Hardy, Bart G. J. Knols, Betwel J. Msugupakulya, Marceline Finda, Najat Kahamba, Edward Thomsen, Ayman Ahmed, Sarah Zohdy, Prosper Chaki, Peter DeChant, Kimberly Fornace, Nicodem Govella, Steven Gowelo, Emmanuel Hakizimana, Busiku Hamainza, Jasper N. Ijumba, William Jany, Hmooda Toto Kafy, Emmanuel W. Kaindoa, Lenson Kariuki, Samson Kiware, Eliningaya J. Kweka, Neil F. Lobo, Dulcisária Marrenjo, Damaris Matoke-Muhia, Charles Mbogo, Robert S. McCann, April Monroe, Bryson Alberto Ndenga, Halfan S. Ngowo, Eric Ochomo, Mercy Opiyo, Richard Reithinger, Chadwick Haadezu Sikaala, Allison Tatarsky, David Takudzwa, Fedra Trujillano, Ellie Sherrard-Smith

“…What is needed now is a significant boost in the financial resources and public health administration structures necessary to train, employ and deploy local-level workforces tasked with suppressing mosquito populations in scientifically driven and ecologically sensitive ways. …”
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