Author Correction: Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

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Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming by Maressa O. Camilo, Romero F. Carvalho, Ariany B.S. Costa, Esly F.C. Junior, Andréa O.S. Costa, Robson C. Sousa

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Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study by Taehwan Kim, Jung-Yeon Choi, Myung Jin Ko, Kwang-il Kim

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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer by Shrey S. Sukhadia, Christoph Sadée, Olivier Gevaert, Shivashankar H. Nagaraj

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A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model by Leonardo Talero-Sarmiento, Sebastian Roa-Prada, Luz Caicedo-Chacon, Oscar Gavanzo-Cardenas

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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

“…After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. …”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

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Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis by Youness Chawki, Khalid Elasnaoui, Mohamed Ouhda

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Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity by Aishwaryo Ghosh, Amitava Moitra, Tanusri Saha-Dasgupta

Subjects: “…machine learning…”
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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

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Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometerZenodo by Colleen Partida, Jose Lucas Safanelli, Sadia Mannan Mitu, Mohammad Omar Faruk Murad, Yufeng Ge, Richard Ferguson, Keith Shepherd, Jonathan Sanderman

“…All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. …”
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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

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Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China by Xinghong Guo, Mingze Ma, Yiyang Chen, Zhaoyang Fang, Jingru Liu, Shuming Yan, Yifei Feng, Xinya Cheng, Jian Wu, Beizhu Ye

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What We Didn’t Know a Recipe Could Be: Political Commentary, Machine Learning Models, and the Fluidity of Form in Nineteenth-Century Newspaper Recipes by Avery Blankenship

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Desain Penilaian Risiko Privasi pada Aplikasi Seluler Melalui Model Machine Learning Berbasis Ensemble Learning dan Multiple Application Attributes by R. Ahmad Imanullah Zakariya, Kalamullah Ramli

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FArSS: Fast and Efficient Semantic Question Similarity in Arabic by Mohamed Alkaoud

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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning by David C Currow, Magnus Per Ekström, Max Olsson

“…Objective To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning.Design and setting This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019.Participants A total of 475 older men aged 73 years from the VASCOL dataset.Measures Self-perceived health was measured using the first item of the Short Form 12. …”
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Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis by Clara K Chow, Aravinda Thiagalingam, Rohan Jayasinghe, Sarah Zaman, Stephen Bacchi, Justin Chan, Aashray Gupta, Shaun Evans, Pramesh Kovoor, Brandon Stretton, Jayme Bennetts, Ammar Zaka, Naim Mridha, Joshua Kovoor, Gopal Sivagangabalan, Cecil Mustafiz, Daud Mutahar, Shreyans Sinhal, James Gorcilov, Benjamin Muston, Fabio Ramponi, Dale J Murdoch

“…Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. …”
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Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications by Mirjam Schöneck, Nicolas Rehbach, Lars Lotter-Becker, Thorsten Persigehl, Simon Lennartz, Liliana Lourenco Caldeira

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