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Machine Learning

General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites

141,764 datasets

Machine Learning

Explainable ML Model for COVID-19 Mortality Risk in Older Adults Using Routine Blood Tests

Lin Luo's research dataset underpins a machine learning model for predicting mortality risk in older adults with COVID-19. The model was developed using routine hematological indicators from 2393 patients and optimized via Bayesian optimization. The Light Gradient Boosting Machine (LGBM) model achieved an AUC of 0.973, and the study identifies top 10 key biomarkers like CRP and D-dimer.

TabularMachine LearningClinical BiomarkersMortality RiskBenchmarkHealthcareCovid 19Explainable Ai+1

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Machine Learning

Machine Learning for Alzheimer's Disease Progression Under Extreme Class Imbalance

A 2026 study by Patrick O. Akinwumi analyzes 3,240 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to predict diagnostic worsening within 24 months. It uses baseline demographic, clinical, and cognitive variables to train XGBoost and logistic regression models under extreme class imbalance. The dataset includes model performance metrics and SHAP analyses for feature interpretation.

TabularMachine LearningAlzheimers DiseaseClinical PredictionBenchmarkHealthcareMedical ResearchNeuroimaging+1

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Machine Learning

Sudden Hearing Loss Prognosis Predictors Under Integrated Therapy

227 patient records with unilateral sudden sensorineural hearing loss were used to train an explainable machine learning model. The XGBoost model achieved an AUC of 0.718 in validation, identifying key predictors like activated partial thromboplastin time and platelet count. The dataset was created by Xiaoxiao Ye and last updated on June 1, 2026.

TabularMachine LearningTraditional Chinese MedicineHearing LossHealthcareMedical DataClinical Prognosis+1

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Machine Learning

PET/CT Feature Clusters for 289 Fever of Unknown Origin Patients

A retrospective cohort of 289 adult patients with fever or inflammation of unknown origin who underwent 18F-FDG PET/CT imaging. The dataset, created by Sule Ceylan and last updated in May 2026, contains features extracted from PET/CT scans, including organ involvement patterns and SUVmax values, which were analyzed using four unsupervised clustering algorithms. The consensus clustering identified four distinct imaging phenotypes, which were compared to final clinical diagnoses.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkHealthcareClinical ResearchPet Ct+1

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Machine Learning

PET/CT Imaging Features and Clusters for Fever of Unknown Origin

A retrospective cohort of 289 adult patients with fever of unknown origin (FUO) or inflammation of unknown origin (IUO) who underwent 18F-FDG PET/CT imaging. The dataset contains extracted imaging features, including organ system involvement, maximum SUVmax values, and uptake patterns, which were used to derive four distinct patient clusters via consensus unsupervised learning. The clusters represent PET/CT phenotypes that showed low concordance with final clinical diagnoses, indicating they capture imaging patterns rather than specific disease etiologies.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkClinical PhenotypesHealthcarePet Ct+1

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Machine Learning

PET/CT Features for 289 Fever of Unknown Origin Patients

Sule Ceylan published a dataset on 2026-05-29 containing results from a retrospective cohort study of 289 adult patients with fever of unknown origin (FUO). The data includes PET/CT imaging features such as organ involvement, SUVmax values, and uptake patterns, which were used for unsupervised clustering analysis. The study aimed to classify patients based on imaging phenotypes and explore their relationship with final clinical diagnoses.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkHealthcareClinical ResearchPet Ct+1

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Machine Learning

PET/CT Feature Clusters for 289 Fever of Unknown Origin Patients

A retrospective cohort study of 289 adult patients with fever or inflammation of unknown origin who underwent 18F-FDG PET/CT imaging. The dataset includes extracted features on organ involvement and SUVmax values, used for unsupervised clustering to identify imaging phenotypes. The data was authored by Sule Ceylan and last updated on 2026-05-29.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkClinical PhenotypesHealthcarePet Ct+1

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Machine Learning

PET/CT Imaging Features for 289 Fever of Unknown Origin Patients

A 2026 retrospective cohort study of 289 adult FUO patients with a mean age of 59 years. The dataset, authored by Sule Ceylan, contains PET/CT imaging features used for unsupervised clustering to identify distinct imaging phenotypes, though these clusters showed low concordance with final clinical diagnoses.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkHealthcareClinical ResearchPet Ct+1

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Machine Learning

PET/CT Feature Clusters for 289 Fever of Unknown Origin Patients

Sule Ceylan's dataset contains results from a retrospective cohort study of 289 adult patients with fever of unknown origin (FUO). The data includes consensus clustering results based on PET/CT imaging features, such as organ involvement and SUVmax, derived using four unsupervised algorithms. The dataset was last updated on 2026-05-29.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkHealthcareClinical ResearchPet Ct+1

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Machine Learning

PET/CT Feature Clusters for 289 Fever of Unknown Origin Patients

A dataset of 289 adult patients with fever or inflammation of unknown origin (FUO/IUO) who underwent 18F-FDG PET/CT imaging. The data includes extracted features on organ and vessel involvement, SUVmax values, and uptake patterns, which were used in a consensus clustering analysis to identify four distinct imaging phenotypes. The dataset was created by Sule Ceylan and last updated in May 2026.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkHealthcareClinical ResearchPet Ct+1

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Machine Learning

PET/CT Imaging Features for 289 Fever of Unknown Origin Patients

A retrospective cohort of 289 adult FUO patients with a mean age of 59 years, comprising 128 women and 161 men. The dataset, created by Sule Ceylan and last updated in May 2026, contains PET/CT imaging features extracted for unsupervised clustering analysis. It includes data on organ involvement patterns and SUVmax values to explore imaging phenotypes.

TabularUnsupervised ClusteringFever Of Unknown OriginMedical ImagingBenchmarkClinical PhenotypesHealthcarePet Ct+1

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Machine Learning

Laminar Burning Velocities for 3,320 Oxygenated Fuel Measurements

An artificial neural network model and dataset for predicting laminar burning velocities of oxygenated fuels. The dataset contains 3,320 experimental measurements for fuels including alcohols, ethers, aldehydes, ketones, esters, and hydrocarbons. The model was developed by Abdul Gani Abdul Jameel and published on figshare in May 2026.

TabularExcelCombustion ScienceFuel PropertiesExperimental MeasurementsThermophysical DataNeural NetworkSynthetic+1

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Machine Learning

Benchmark-Grade Fixed-Point 3D Spatial Navigation Pipeline in C++

A production-ready C++ codebase for a high-performance 3D spatial navigation and hardware signal ingestion pipeline. The repository is 7.3 KB in size and was authored by Jamie Davis, last updated on June 1, 2026. It is licensed under CC-BY-4.0 and hosted on figshare.

TextTabularGeospatialHardware Signal ProcessingSpatial navigationBenchmarkReal Time ProcessingCplusplusEmbedded Systems+1

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Machine Learning

Data Sheet 1_Machine learning based on body composition radiomics for predicting early rec

A multicenter study by Yongjie Zhou, last updated in 2026, developed a machine learning model using preoperative CT body composition radiomics to predict early recurrence in colorectal cancer. The dataset includes 917 patients from three institutions, partitioned into training and external test sets. An 11-feature radiomics signature was identified, with Random Forest models achieving AUCs up to 0.807.

TabularMachine LearningRadiomicsMedical ImagingClinical PredictionHealthcareColorectal cancer+1

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Machine Learning

Binary Build Prediction Metrics from TravisTorrent and GHALogs, 2013-2023

175,706 builds from two open-source CI/CD platforms, TravisTorrent (100,000 builds, 2013–2017) and GHALogs (75,706 workflows, 2023), form the basis for evaluating machine learning models that predict build success. The dataset, created by Lalit Narayan Mishra and last updated in May 2026, provides metrics for a study on preventing temporal data leakage in build prediction. It demonstrates that removing leaky features reduces reported accuracy by up to 15.07 percentage points, revealing a more realistic performance baseline.

TabularTime SeriesExcelMachine Learning EvaluationSoftware EngineeringTemporal LeakageBenchmarkCi CdBuild PredictionSynthetic+1

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Machine Learning

CI/CD Build Success Prediction Data from Travis and GitHub Actions

175,706 automated software build records from two open-source CI/CD platforms, TravisTorrent and GHALogs, spanning 10 years from 2013 to 2023. The dataset supports research on predicting build success while avoiding temporal data leakage, a methodological flaw identified by a three-type taxonomy. It was created by Lalit Narayan Mishra and is licensed under CC-BY-4.0.

TabularTime SeriesExcelMachine LearningSoftware EngineeringTemporal LeakageCi CdBuild PredictionSynthetic+1

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Machine Learning

Build Success Predictions from TravisTorrent and GHALogs, 2013-2023

175,706 automated software builds from two open-source CI/CD platforms spanning 10 years, collected by Lalit Narayan Mishra. The data supports a study on temporal data leakage in machine learning models for predicting build success or failure. Models using only pre-build features achieved 82.73% accuracy on TravisTorrent (2013-2017) and 83.30% on GHALogs (2023).

TabularTime SeriesExcelMachine LearningSoftware EngineeringTemporal LeakageCi CdBuild PredictionSynthetic+1

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Machine Learning

Cross-Domain Plant Disease Classification Reliability Benchmark

Kun Xiang's dataset quantifies the reliability gap for machine learning models transferring from controlled lab to field conditions. It contains results from a benchmark study applying techniques like temperature scaling and selective prediction to a ResNet-50 model fine-tuned on PlantVillage and evaluated on PlantDoc. The dataset was last updated on 2026-05-22 and is shared under a CC-BY-4.0 license.

TabularCSVHealthcareComputer VisionDomain ShiftAgricultural AiPlant Disease ClassificationReliability Benchmark+1

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Machine Learning

Cross-Domain Plant Disease Classification Reliability Gap Benchmark

A 1.4 KB CSV file by Kun Xiang, last updated May 22, 2026, quantifies the reliability gap in plant disease classifiers moving from controlled to field imagery. It benchmarks standard mitigation techniques like temperature scaling and selective prediction, showing a 67.7-percentage-point accuracy collapse under domain shift. The analysis includes quantitative image-level shift metrics and performance results for models like ResNet-50 and DINOv2 across datasets PlantVillage and PlantDoc.

TabularCSVHealthcareComputer VisionAgricultureDomain ShiftPlant Disease ClassificationReliability Benchmark+1

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Machine Learning

Northern Hemisphere Monthly Snow Cover Extent from Satellite Observations

1966 to 2001 satellite-derived data shows monthly snow cover extent across the Northern Hemisphere at multiple spatial scales (1.0, 0.5, and 0.25 degree). This dataset, derived from the NSIDC EASE-Grid product and provided by NASA, represents the percentage of days with snow present each month. It was the first combined representation of snow and sea ice measurements from satellites for this period.

TabularTime SeriesGeospatialZIPTextClimatologySnow CoverNorthern HemisphereCryosphereSatellite Observations+1

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