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%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests

BMC MEDICAL INFORMATICS AND DECISION MAKING 2025年第1期25卷 1-16页

作者： Muthusi, Jacques K. Young, Peter W. Mboya, Frankline O. Mwalili, Samuel M. US Ctr Dis Control & Prevent Div Global HIV & TB Ctr Global Hlth POB 606 Nairobi 00621 Kenya US Ctr Dis Control & Prevent Mozamb Global Hlth Ctr Div Global HIV & TB Maputo Mozambique Strathmore Univ Ctr Hlth Analyt & Modelling CHaM Inst Math Sci Nairobi Kenya

BackgroundMeasures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data. We developed a SAS macro for evaluating multiple diagnostic tests using individual-level data that creates a 2 x 2 summary table, AUROC and AUPRC as part of *** SAS macro presented here is automated to reduce analysis time and transcription errors. It is simple to use as the user only needs to specify the input dataset, "standard" and "test" variables and threshold values. It creates a publication-quality output in Microsoft Word and Excel showing more than 15 different accuracy measures together with overlaid AUROC and AUPRC graphics to help the researcher in making decisions to adopt or reject diagnostic tests. Further, it provides for additional variance estimation methods other than the normal distribution *** tested the macro for quality control purposes by reproducing results from published work on evaluation of multiple types of dried blood spots (DBS) as an alternative for human immunodeficiency virus (HIV) viral load (VL) monitoring in resource-limited settings compared to plasma, the gold-standard. Plasma viral load reagents are costly, and blood must be prepared in a reference laboratory setting by a qualified technician. On the other hand, DBS are easy to prepare without these restrictions. This study evaluated the suitability of DBS from venous, microcapillary and direct spotting DBS, hence multiple diagnostic tests which were compared to plasma specimen. We also used the

关键词： SAS macro Disease prevalence Diagnostic accuracy measures Reproducible research machine learning classification

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Rapid, label-free detection of colorectal cancer with an aberration-free line scanning confocal Raman imager

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 339卷 126182页

作者： Jiao, Changwei Chen, Miaoliang Liao, Jiaqi Li, Jialun Zhang, Ruili He, Sailing Zhejiang Univ Coll Opt Sci & Engn Ctr Opt & Electromagnet Res Hangzhou 310058 Peoples R China Zhejiang Univ Taizhou Hosp Zhejian Prov MedEngInfo Collaborat Res Ctr Zhejiang Engn Res Ctr Intelligent Med Imaging Sens Taizhou Peoples R China Zhejiang Univ Natl Engn Res Ctr Opt Instruments Hangzhou 310058 Peoples R China Zhejiang Univ Taizhou Hosp Zhejian Prov Gastrointestinal Surg Dept Taizhou Peoples R China Royal Inst Technol Sch Elect Engn Dept Electromagnet Engn S-10044 Stockholm Sweden

A self-developed aberration-free line scanning confocal Raman imager (AFLSCRI) with a spectral resolution of 0.12 nm and a spatial resolution of 2 mu m is utilized to diagnose colorectal cancer. The tissues were categorized into four subgroups (typical tissue, lipid-rich tissue, fat-rich tissue, and collagen-rich tissue) and were successfully distinguished with our Raman imaging results. Compared to traditional point-scanning Raman spectroscopy, this imager offers a much faster speed with high spectral resolution while maintaining a similar spatial resolution. The Raman spectroscopy results of the same sample of colorectal cancer remain stable and unaffected even measured after six months. The molecular composition of the tissues was analyzed, and potential biomarkers such as carotenoids and protein structures were identified for four different types of colorectal tissues. When combined with machine learning algorithms, an accuracy of 92.8% was achieved in identifying 14 pairs of normal/cancer samples. These results highlight the great potential of the AFLSCRI in label-free, rapid, and noninvasive medical diagnosis.

关键词： Colorectal cancer Aberration free Line scanning Confocal Raman imager machine learning classification

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Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda 23

Addressing contingency in algorithmic (mis)information class...

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6th ACM Conference on Fairness, Accountability, and Transparency (FAccT)

作者： Hernandez, Andres Dominguez Owen, Richard Nielsen, Dan Saattrup Mcconville, Ryan Univ Bristol Bristol England Alexandra Inst Aarhus Denmark

ISBN: (纸本)9781450372527

machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of “truth” used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies—key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.

关键词： Misinformation Content Moderation Fact-checking machine learning classification Responsible ML

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A novel UPLC-based method to identify elephant and mammoth ivory

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SCIENTIFIC REPORTS 2025年第1期15卷 1-10页

作者： Su, Chang Xie, Yingshuang Wang, Bo Cui, Liangyu Liu, Boyang Zhou, Yongheng Yang, Shuhui Zhang, Lifeng Xu, Yanchun Northeast Forestry Univ Coll Wildlife & Protected Area Harbin 150040 Peoples R China Lanzhou Customs Technol Ctr Food Safety Testing Lab Lanzhou 730000 Peoples R China Sci & Technol Res Ctr China Customs Beijing 100730 Peoples R China Natl Forestry & Grassland Adm Res Ctr Engn Technol Harbin 150040 Peoples R China

The illegal ivory trade has led to a sharp decline in wild elephant populations in recent decades, while mammoth (Mammuthus primigenius) ivory products have increasingly flooded the marketplace, complicating identification efforts by frontline law enforcement. Existing ivory identification methods face several challenges, including inconsistent accuracy due to sample quality, high costs, slow turnaround times, and destructive sample requirements. Currently, there is a lack of a fast, convenient, and highly effective solution to address these issues. To tackle these challenges, this study developed a novel, efficient, and accurate method for identifying and characterizing ivory products using ultra-performance liquid chromatography with a fluorescence detector (UPLC-FLR). The technique focuses on various organic small-molecule compounds in ivory that have been largely overlooked. A chemical fingerprint library was created using 72 African elephant and 69 mammoth ivory samples. Comprehensive comparative analyses, including principal component analysis, compound distribution analysis, proportion analysis, chord diagrams, and statistical significance tests, revealed significant differences between the organic compounds in the two sample groups, confirming the method's reliability. Six widely used machine learning classification models were then applied to construct a discriminant model based on 11 key feature compounds among the 85 identified, with each model achieving 100% classification accuracy. Compared to the conventional 'gold standard' molecular biology method, this UPLC-based approach shortened detection time from 24 h to just 1 h, reduced the sample requirement by 50%, and cut costs by 90%, making it a more efficient, user-friendly solution for frontline law enforcement. Widespread adoption of this method in law enforcement could become a powerful tool in the fight against the illegal ivory trade.

关键词： Ivory UPLC-FLR Chemical fingerprinting machine learning classification

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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces

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SENSORS 2025年第9期25卷 2713-2713页

作者： Philippopoulos, Panos I. Koutrakis, Kostas N. Tsafaras, Efstathios D. Papadopoulou, Evangelia G. Sigalas, Dimitrios Tselikas, Nikolaos D. Ougiaroglou, Stefanos Vassilakis, Costas Univ Peloponnese Digital Syst Dept GR-23100 Sparta Greece Univ Peloponnese Informat & Telecommun Dept GR-22100 Tripolis Greece Int Hellen Univ Dept Informat & Elect Engn GR-57400 Thessaloniki Greece

RSSI-based proximity positioning is a well-established technique for indoor localization, featuring simplicity and cost-effectiveness, requiring low-price and off-the-shelf hardware. However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. Our work reports and discusses findings of a BLE/RSSI-based pilot, implemented at the Museum of Modern Greek Culture in Athens, involving eight buildings with 47 halls with diverse areas, shapes, and showcase layouts. Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. Visitor movement modeling, based on critical parameters influencing signal measurements, provided scenarios mapped to popular behavioral models. One such model, "ant", corresponding to relatively slow nomadic cell roaming, was selected for basic experimentation. Pilot implementation decisions and methods adopted at all layers of the VTT architecture followed the overall concept of simplicity, availability, and cost-efficiency, providing a maximum infrastructure cost of 8 Euro per m2 covered. A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. RSSI data, temporal and spatial management with simple low-processing methods adopted, achieved a maximum prediction accuracy average of 81.53

关键词： large/complex museums indoor proximity positioning Bluetooth Low Energy RSSI temporal/spatial methods machine learning classification museum visitor modeling

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Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability

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MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2025年第1期38卷 23-35页

作者： Panahi, Mehdi Habibi, Maliheh Hosseini, Mahboube Sadat Payame Noor Univ Erbil Branch Dept Comp Engn Erbil Iraq Payame Noor Univ Birjand Branch Dept Comp Engn Birjand Iran Shahid Beheshti Univ Dept Med Radiat Engn Tehran Iran

ObjectiveThis study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning ***1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype *** harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC >= 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after *** harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

关键词： Radiomics Magnetic resonance imaging Parkinson's disease Gray-level discretization Feature harmonization machine learning classification

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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年 232卷

作者： Tan, Mas Ira Syafila Mohd Hilmi Jamlos, Mohd Faizal Omar, Ahmad Fairuz Kamarudin, Kamarulzaman Jamlos, Mohd Aminudin Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Malaysia Univ Sains Malaysia Sch Phys George Town 11800 Penang Malaysia Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra 02600 Perlis Malaysia Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra 02600 Perlis Malaysia

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR's sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900-1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of similar to 1450 nm which reflectance of infected sample is higher 0.2-0.4 than healthy sample which 0.1-0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs' spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different

关键词： Oil palms Near-infrared spectroscopy machine learning classification

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Environmental sustainability: A machine learning approach for cost analysis in plastic recycling classification

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RESOURCES CONSERVATION AND RECYCLING 2023年第1期197卷

作者： Carrera, Berny Mata, Judit Bazin Pinol, Victor Luid Kim, Kwanho Incheon Natl Univ Ind & Management Engn Songdodong 119 Acad ro Incheon 22012 South Korea Univ Politecn Cataluna Ind Engn Avinguda Diagonal 647 Barcelona 08028 Spain

Recycling plastics can reduce waste generation and improve waste management, but the recycling industry needs both cost reduction and increased revenue to be economically viable. Recently, recycling plastic classification techniques with Artificial Intelligence have gained popularity, as they can avoid manual sorting, which is timeconsuming and economically less profitable than automatidc processing. In this paper, we provide an economic framework for quality sorting control by classifying plastics based on the infrared spectrum of polymers and machine learning algorithms. In addition, the suggested framework offers a method for selecting the algorithm according to the polymer's income class and the highest economic advantages. Furthermore, our experiments probe that Fourier-transform infrared (FTIR) and near-infrared (NIR) spectroscopies combined with machine learning algorithms are suitable for plastic classification as four datasets and seven machine learning algorithms have been tested to classify Polyethylene (PE), Polypropylene (PP), Polyethylene terephthalate (PET), polystyrene (PS), and Polyvinyl chloride (PVC).

关键词： Plastic recycling revenue Profitable plastics Plastic recycling industry Waste management Optimization machine learning classification Environment Sustainable cities Quality sorting

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Exploring incel group dynamics: a computational study of hierarchy and group-boundary policing

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JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2025年第2期8卷 1-25页

作者： Solopova, Veronika Popa-Wyatt, Mihaela Berskyte, Justina Tech Univ Berlin Elect Syst Med Engn Einsteinufer 17 D-10587 Berlin Germany Univ Manchester Philosophy Dept Oxford Rd Manchester M13 9PL England

Incels (involuntary celibates) are part of a broader misogynistic culture known as the manosphere. Some communities within the manosphere, including incels, promote gender-based violence through misogynistic rhetoric and ideology. Incels are men who struggle to form romantic relationships and thus seek solace in online forums to find a sense of purpose and community. The community is organised around an ideology and a hierarchical classification of members. This paper presents a computational linguistic analysis of the utterances made within the community. We analyse the linguistic patterns of six different ranks of users: paragons, high-rank users, low-rank users, greycels, banned users, and self-banned/deleted users. We conducted an analysis of the sentiments expressed between ranks and of the affective attitude of posters when discussing a variety of topics. We then we analysed linguistic features, also conditioned on the rank of the poster. These analyses reveal qualitative differences between users of different ranks in how they express themselves and their sentiments. Finally, we trained models based on the linguistic features of users' posts and show that already based on this information it is possible to predict the rank of users. Overall, our results indicate that the incel community has developed distinctive linguistic patterns to disseminate their ideology and that these linguistic patterns vary according to the rank of an incel within the community. This methodology offers a robust framework for studying similar echo chamber-like communities.

关键词： Incels Hierarchy Status Group-boundary policing Inter-rank dynamics and emotional patterns Sentiment analysis machine learning classification

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machine learning Based Abnormal Gait classification with IMU Considering Joint Impairment

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SENSORS 2024年第17期24卷 5571页

作者： Hwang, Soree Kim, Jongman Yang, Sumin Moon, Hyuk-June Cho, Kyung-Hee Youn, Inchan Sung, Joon-Kyung Han, Sungmin Korea Inst Sci & Technol KIST Res Ctr B Biomed Res Div Seoul 02792 South Korea Korea Univ Sch Biomed Engn Seoul 02841 South Korea Korea Univ Coll Med Dept Neurol Anam Hosp Seoul 02841 South Korea Korea Univ Sci & Technol KIST Sch Div Biomed Sci & Technol Seoul 02792 South Korea Kyung Hee Univ KHU KIST Dept Converging Sci & Technol Seoul 02447 South Korea

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.

关键词： abnormal gait joint impairment IMU-based system walkway system RFECV machine learning classification

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