A Python package for AI-based comprehensive analysis of sarcomere structure and function in cardiomyocytes

This project comprises predicting different types of disease at one place Pneumonia, Malaria, Liver Disease and Cardiovascular Disease

Cystatin C and Cardiovascular Disease: A Mendelian Randomization Study.

A package for running experiments with VAEs for ECG data, including a supervised head designed for survival analysis of cardiovascular disease events.

The RNN for Cardiovascular Disease Detection project is an innovative application of deep learning techniques to detect and predict cardiovascular diseases using recurrent neural networks (RNNs). Built using Python, TensorFlow, and Keras, this project aims to provide a reliable tool for early detection and diagnosis of cardiovascular diseases.

Code for paper "Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance", Bonnemain et al., 2021, Frontiers in Cardiovascular Medicine

Comprehensive collection of 8 clinical data science and health analytics projects focusing on disease prediction, risk stratification, and treatment pattern analysis using advanced machine learning algorithms and statistical modeling. Portfolio: https://nana-safo-duker.github.io/

An end to end ML model to predict whether a person has cardiovascular disease or not based on various features.

A robust heart disease risk assessment tool built with Python and Streamlit. Utilizes an Ensemble Stacking Classifier (Random Forest, XGBoost, SVM) to predict cardiovascular disease with high accuracy, complete with interactive visualizations and medical insights.

An R script doing a basic statistical analysis and modeling of CVD/excess death trends in the U.S.

Emulated target trial analyzing workplace health assessments and cardiovascular risk in 2,091,421 Swedish workers

Analyses on gut microbiota composition and longitudinal new-onset cardiometabolic diagnoses in the HELIUS cohort.

Published research (Springer ICTCS 2024) evaluating ML architectures for cardiovascular risk prediction. Features text-to-speech accessibility modules.

Ensemble Learning

Analysis of patient data from a kaggle dataset to assess if tall people's risk of developing cardiovascular disease was higher than short people's.

Multi-tier explainable AI framework for cardiovascular disease prediction using XGBoost, SHAP, LIME, Anchors, and LLM-based reporting

Cardio-vascular-disease-prediction

Stress driven glutamate, calcium, and ROS disease pathway research with a focus on transgenerational heritability. Source vault for the Biolectrics Wiki.

Comparative analysis of machine learning models for cardiovascular disease prediction using supervised learning techniques.