Advanced machine learning model for early diabetes detection using 8 key clinical and demographic parameters.
This model uses 8 carefully selected clinical and demographic parameters to predict the likelihood of diabetes with high accuracy. It's designed for quick screening in primary care settings.
Best for: Initial screening, routine check-ups, and population health programs where quick assessment is needed.
Limitations: Does not include lifestyle factors or more comprehensive metabolic markers.
Advanced machine learning model for diabetes detection using 43 clinical, lifestyle, and biochemical parameters.
This comprehensive model provides a detailed diabetes risk assessment by analyzing 43 different parameters including clinical measurements, lifestyle factors, and family history.
Best for: Comprehensive risk assessment, personalized prevention plans, and specialty diabetes clinics.
Output includes: Risk score, personalized recommendations, and predicted progression timeline.
Balanced diabetes prediction model using 17 clinical and demographic parameters for accurate assessment.
This mid-range model offers an optimal balance between simplicity and comprehensiveness, using 17 carefully selected features for diabetes prediction in clinical settings.
Best for: Routine clinical practice, health screenings, and epidemiological studies where a balance of accuracy and practicality is needed.
Advantage: Provides 85% of the predictive power of the comprehensive model with half the inputs.
Predicts body site origin (Mouth, Nasal, Skin, Stool) of microbiome samples from sequencing characteristics or FastQ files.
This neural network model predicts the body site origin of microbiome samples with exceptional accuracy, supporting both manual feature entry and FastQ file uploads for automated analysis.
Best for: Microbiome research, clinical diagnostics, forensic analysis, and quality control in sequencing pipelines.
Output includes: Predicted origin with confidence score, probability distribution across all sites, and detailed characteristics of the predicted location.
Technical Note: Model built with TensorFlow.js for browser-based execution, with feature extraction algorithms optimized for microbiome sequencing data.
Deep learning model for early detection of 26 different cancer types from medical imaging and biomarkers.
This advanced deep learning model can identify 26 different types of cancer from various medical imaging modalities and biomarker patterns, providing early detection capabilities.
Best for: Early cancer screening programs, diagnostic support in radiology, and integration with electronic health records for risk stratification.
Output includes: confidence scores, top 5 predictions, suspicious areas highlighted, and report generation.
CNN-based model for tuberculosis identification from chest X-ray images with high accuracy.
This convolutional neural network (CNN) model specializes in detecting tuberculosis from standard chest X-ray images, providing rapid screening comparable to expert radiologists.
Best for: TB screening programs, high-risk population monitoring, and resource-limited settings with limited access to radiologists.
Deep neural network for pneumonia detection and classification in chest radiographs.
This deep learning model accurately identifies pneumonia from chest X-rays, distinguishing between viral and bacterial pneumonia with supporting clinical data.
Best for: Emergency departments, primary care settings, and pediatric clinics where rapid pneumonia diagnosis is critical.
Output includes: Confidence score, likely etiology, and recommended antibiotic class if bacterial.
Comprehensive model for cardiovascular disease risk prediction using health metrics and lifestyle factors.
This model evaluates risk factors for various cardiovascular diseases including coronary artery disease, heart failure, and arrhythmias, providing personalized risk assessments.
Best for: Preventive cardiology, primary care risk assessment, and cardiac rehabilitation programs.
Output includes: 10-year risk prediction, modifiable risk factors, and evidence-based prevention strategies.
AI model for breast cancer detection from mammograms, ultrasounds, and clinical data.
This specialized model analyzes clinical data to detect breast cancer, providing radiologists with decision support and second opinions.
Best for: Breast cancer clinical analysis, diagnostic radiology departments, and high-risk patient monitoring.
Deep neural network for malaria parasite detection and species identification from blood smear images.
This AI model identifies malaria parasites in blood smear images, providing rapid diagnosis comparable to expert microscopy with species identification capabilities.
Best for: Areas with limited access to trained microscopists, field clinics, and malaria control programs.