Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. …”
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An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers by Bogong Liu, Huichao Liu, Junhao Tu, Jian Xiao, Jie Yang, Xi He, Haihan Zhang

“…The prediction accuracy of above algorithms could be optimized using genome-wide association study (GWAS) to select subsets of significant SNPs. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests by Jacques K. Muthusi, Peter W. Young, Frankline O. Mboya, Samuel M. Mwalili

“…We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease. Conclusion The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. …”
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Xpert Ultra stool testing to diagnose tuberculosis in children in Ethiopia and Indonesia: a model-based cost-effectiveness analysis by Peter J Dodd, Nyashadzaishe Mafirakureva, Rina Triasih, Debebe Shaweno, Jens Levy, Eveline Klinkenberg, Petra de Haas, Ineke Spruijt, Nastiti Kaswandani, Ahmed Bedru, Melaku Gebremichael, Edine W Tiemersma

“…The intervention remained cost-effective under sensitivity analyses.Conclusions The addition of the SOS stool method to national algorithms for diagnosing TB in children is likely to be cost-effective in both Ethiopia and Indonesia.…”
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Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu by Raga Saputra Heri Istanto, Fitra Abdurrachman Bachtiar, Achmad Ridok

“…These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%. …”
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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. …”
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Three-dimensional linkage analysis with digital PCR for genome integrity and identity of recombinant adeno-associated virus by Tam Duong, Michele Firmo, Chien-Ting Li, Bingnan Gu, Peng Wang

“…Particularly, we rigorously tested our algorithms by conducting experiments involving the mixing of seven DNA fragments to represent various AAV viral genome populations, including 3 single partials, 3 double partials, and 1 full-length genomes. …”
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Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer by Hao Liu, Chuhan Zhang, Sanfei Peng, Yuhan Yin, Yishi Xu, Sihan Wu, Liping Wang, Yang Fu

“…Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. …”
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HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease by Yin-Yuan Su, Hsuan-Cheng Huang, Yu-Ting Lin, Yi-Fang Chuang, Sheh-Yi Sheu, Chen-Ching Lin

“…Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. …”
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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

“…The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. …”
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MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…The novelty consists in the application of fiducial approach; the calculation of critical values are fulfilled with the aid of computer technologies of simulation of possible realizations of the correlation coefficients for the two assumptions, viz. technical and economic indicators of the independent and dependent; simulation is fulfilled with the method of solving the “inverse problem”, which enables the possible implementation of the correlation coefficients for the really dependent and independent samples of random variables at a given sample size; the developed algorithms and programs for calculation made it possible to obtain the critical values of correlation coefficients for independent and dependent samples; in conditions of the sameness of the consequences of erroneous decisions it is proposed to make a decision not based on critical value but based on the boundary values of the correlation coefficients that correspond to the minimum total risk of erroneous decisions; the exemplification of the recommendations application was made on example of technical and economic parameters of boilers of power units of 300 MWt. …”
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Enhancing furcation involvement classification on panoramic radiographs with vision transformers by Xuan Zhang, Enting Guo, Xu Liu, Hong Zhao, Jie Yang, Wen Li, Wenlei Wu, Weibin Sun

“…The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions. Conclusion DL algorithms can automatically classify FI using readily accessible panoramic images. …”
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Cardiovascular Disease Screening in Primary School Children by Alena Bagkaki, Fragiskos Parthenakis, Gregory Chlouverakis, Emmanouil Galanakis, Ioannis Germanakis

“…Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). …”
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Dynamic analysis and optimal control of a hybrid fractional monkeypox disease model in terms of external factors by Saima Rashid, Abdul Bariq, Ilyas Ali, Sobia Sultana, Ayesha Siddiqa, Sayed K. Elagan

“…We remark that in various time scale domains $$\mathbb {N}_{\ell }$$ , the investigated discrete formulations will be $$\rho ^{2}$$ -nonincreasing or $$\rho ^{2}$$ -nondecreasing by examining $$\rho$$ -monotonicity formulations and the basic properties of the suggested operator. Algorithms are constructed in the discrete generalized Mittag–Leffler (GML) kernel for mathematical simulations, emphasizing the effects of the infection resulting from multiple factors. …”
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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study by Yunzhen Ye, Yu Xiong, Qiongjie Zhou, Jiangnan Wu, Xiaotian Li, Xirong Xiao

“…Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. …”
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Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities by Thien B. Nguyen-Tat, Tran Quang Hung, Pham Tien Nam, Vuong M. Ngo

“…This study provides a thorough evaluation of the performance of different preprocessing methods and deep learning algorithms across commonly used medical imaging modalities. …”
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DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

“…The novelty of the article is that for the first time in national and international practice, it proposes an alternative/supplementary algorithmic method for determining the level of certain full and/or partial professional qualifications by the National Qualifications Framework, thus creating prerequisites for further automation of the activities of professional standards developers. …”
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Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP by Zeying Wen, Xiaohe Gao, Qingxia Wu, Jianwei Yang, Jian Sun, Keliu Wu, Hongfei Zhao, Ruihua Wang, Yanmei Li

“…Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithms were used for dimensionality reduction and selection of radiomics features. …”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…The research offers policymakers, transportation authorities, and safety advocates practical insights by utilizing sophisticated machine-learning algorithms and integrating multiple datasets. Road crash fatalities can be decreased and safer transportation systems can be established by using the predictive models that have been created as a proactive tool for identifying high-risk regions and allocating resources for targeted improvements. …”
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