AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING by Muhammad Anas, Muhammad Atif Imtiaz, Saad Khan, Arshad Ali, Noor Fatima Naghman, Hamayun Khan, Sami Albouq

“…The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. …”
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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. …”
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A hybrid approach to financial big data analysis using extended ensemble learning and optimized spark streaming by Muhammad Babar

Subjects: “…Machine learning…”
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Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data by Jun-Song Zhang, Run-Sang Pan, Guo-Lu Li, Jian-Xiang Teng, Hong-Bo Zhao, Chang-Hua Zhou, Ji-Sheng Zhu, Hao Zheng, Xiao-Bin Tian

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Unveiling Immune Response Mechanisms in Mpox Infection Through Machine Learning Analysis of Time Series Gene Expression Data by Qinglan Ma, Xianchao Zhou, Lei Chen, Kaiyan Feng, Yusheng Bao, Wei Guo, Tao Huang, Yu-Dong Cai

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Performance Analysis of Machine Learning Techniques for Server Health Monitoring Using Time Series Data Against DDOS Attacks by Sajja Ratan Kumar, Valli Kumari Vatsavayi

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Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis Based on Machine Learning and Single-Cell Sequencing Data by Qi X, Cao S, Chen J, Yin X

“…Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. …”
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Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data by Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Christian Diers, Christoph Garth, Michael Fröhlich

“…Traditional proportion-based segmentation methods are often limited, particularly for atypical foot structures and low-quality data. Although recent advances in machine learning (ML) offer opportunities for automated and robust segmentation across diverse datasets, existing models primarily rely on data from single laboratories, limiting their applicability to multicenter datasets. …”
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Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis by Fahim Faisal, Antonios Danelakis, Marte-Helene Bjørk, Bendik Winsvold, Manjit Matharu, Parashkev Nachev, Knut Hagen, International Headache Genetics Consortium, Erling Tronvik, Anker Stubberud

“…We aimed to investigate if machine learning could exploit the combination of genetic data and general clinical features to identify individuals at risk for new-onset migraine. …”
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Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield by Xiaoshuai Wu, Yuanliang Zhao, Jianpeng Zhao, Shichen Shuai, Bing Yu, Junqing Rong, Hui Chen

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Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia by Sadashi Horie, Mizuki Yamashita, Kenta Matsumura, Yoji Uejima, Makito Kaneda, Kentaro Nagaoka, Kentaro Tamura, Kenu Harada, Yoshihiro Yamamoto, Isao Kitajima, Chihaya Imai, Hideki Niimi

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Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data by Yilin Chen, Xueqin Huang, Lu Tian

“…With the widespread application of artificial intelligence in medicine, some studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. …”
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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

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Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data by Jan Tiede, Joshua Leon Lovell, Christian Jordan, Armin Moghimi, Torsten Schlurmann

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Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms. by Juntu Li, Linfeng Tao, Yanyou Zhou, Yue Zhu, Chao Li, Yiyuan Pan, Ping Yao, Xuefeng Qian, Jun Liu

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Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data by Masahiro Hata, Yuki Miyazaki, Kohji Mori, Kenji Yoshiyama, Shoshin Akamine, Hideki Kanemoto, Shiho Gotoh, Hisaki Omori, Atsuya Hirashima, Yuto Satake, Takashi Suehiro, Shun Takahashi, Manabu Ikeda

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Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

“…Design/methodology/approach – Through a preprocessing and normalization of data, seven machine learning algorithms were applied. …”
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Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction by Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei, Cosimo Magazzino

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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. …”
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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

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