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Externally validated machine learning models for predicting caesarean section following induction of labour using real-world, population-based administrative datasets

Supervised machine learning pipeline for heart disease risk prediction using clinical data, achieving strong accuracy with interpretable feature analysis

Full-stack healthcare risk prediction demo with FastAPI, React, and scikit-learn for diabetes and heart-disease assessment.

Sex-stratified Deep Survival Machines API for coronary artery disease risk estimation with intervention overlays

End-to-end credit risk ML pipeline with CatBoost, SHAP & LIME explainability, fairness monitoring, and auto-generated PDF reports built for auditability over accuracy.

Interpretable ML for heart disease risk prediction. Logistic Regression vs Random Forest with threshold optimization and live Streamlit dashboard.

AI-powered preventive healthcare risk prediction system.

This study aims to explore the impact of incorporating multiple predictors on diabetes risk prediction by comparing the performance of a multiple linear regression model with age, BMI, and family history against a simpler model using only blood pressure as a predictor.

End-to-end ML app for clinical risk modeling: predicts unstable plaque, plaque volume, lumen area, and adverse outcomes from tabular cardiology data

SyntheticHealthSimulator generates realistic synthetic medical data for machine learning research. It models 20-year health trajectories based on lifestyle factors, biomarkers, and genetic risks. NOT FOR CLINICAL USE.

Privacy-preserving ML system that analyzes 8 driver behavioral signals to predict crash risk in real-time. Stack: Python, Flask, Random Forest, MySQL.

Reproducible QRISK3 10-year cardiovascular risk computation pipeline using UK Biobank Instance 1 baseline data.

GLP-1临床试验风险预测系统

Machine learning web platform for predicting cardiovascular disease risk using ECG signals and data analysis.

End-to-end Machine Learning system for heart attack risk prediction with FastAPI deployment and Docker containerization.

In India’s mountainous areas, maternal mortality is still a serious public health concern, especially in Uttarakhand, where access to healthcare is hampered by geographical obstacles. UttaraRisk-Next, a multi-task ensemble learning framework for thorough maternal health risk assessment, is presented in this paper.

Credit-Scoring-Analysis

ML-based heart disease risk prediction with explainability (SHAP) and Streamlit demo

Interactive dashboard for predicting prediabetes risk using machine learning and SHAP interpretability. Built for clarity, modular benchmarking, and clinical transparency. Includes manual input prediction, threshold-based classification, SHAP visualizations, and model comparison across classifiers.