Wildfire risk assessment using remote sensing data - Prediction of Wildfires

A comparative analysis of 4 ML algorithms. This Hypertension Risk Prediction Model can be described as a machine learning model designed to predict an individual's risk of developing hypertension based on various input parameters.

Deep learning to estimate lung-related mortality from chest radiographs.

A machine learning algorithm to create risk score models for risk prediction

MIEO (Masked Input Encoded Output): self-supervised embeddings for clinical tabular data; handles missing values and mixed types for CVD risk prediction.

MICCAI 2024: Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms

Prognostic ML-models and key-feature extraction for analysis of cardiovascular complications

Acute Lung Injury Code for Paper Submitted to AMIA. Experimented with a wide range of ML algorithms to predict the risk of Acute Lung Injury for intensive care unit patients in 24-hour intervals using demographic and clinical observation features.

multiPGS_py is a fast, simple and low-memory python method to calculate polygenic scores (PGS/PRS)

Digital lifestyle benchmark dataset (3,500 synthetic records, 24 features) for mental wellbeing risk modeling (high_risk_flag) + reproducible notebooks.

A machine learning–powered early warning system for universities that identifies students at academic or scholarship risk through predictive modeling, visualization, and explainable decision support.

It is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.

Educational diabetes risk awareness web app — XGBoost model trained on BRFSS big data (2020–2024) using Google BigQuery & Cloud Storage + personalized prevention advice powered by Gemini 2.5-flash. Built with Streamlit. For Canada / North America.

Text Classification on reddit data for eRisk CLEF 2020 on the task of Risk Prediction.

Risk and Predictive Analytics in the Area of Car Insurance Planning and Marketing

Machine learning system that predicts heart disease risk using patient health data and visual insights

Graph based AI for early detection of underperformance in educational and organizational contexts implemented with Jac

SKLEARN-Credit Risk Prediction Using Logistic Regression Model, ML, Confusion Matrix, classification Report

AI-Driven Supply Chain Risk Analysis & Black-Box Optimization Platform

GLP-1临床试验风险预测系统

Create risk assessment model on parsed text medical records

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

🚀 Orchestrate your machine learning workflows with RoadML Pipeline, an enterprise solution for managing end-to-end ML lifecycle efficiently.

Predicting traffic accident risk using Physics-Informed Feature Engineering and Advanced Stacking Ensemble (XGBoost, LightGBM, CatBoost).

Industrial Thesis in Machine Learning for the achievement of Master of Science in Computer Science.

🤖 AI-powered Scrum automation toolkit with Slack/Trello integration, risk prediction, and task prioritization. Features ML models and Streamlit dashboard.

Cardiovascular disease risk prediction API built with FastAPI, SQLite and Scikit-learn including ML pipeline, model deployment and automated testing.

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.

Healthcare AI Assistant Pro is an advanced analytics platform that leverages machine learning and artificial intelligence to predict patient readmission risks. This enterprise-grade solution provides healthcare institutions with data-driven insights to improve patient care, reduce readmission rates, and optimize resource allocation.

End-to-end machine learning project for predicting coronary heart disease risk using structured clinical data, focusing on class imbalance, model evaluation trade-offs, and explainability.