Tag: heart decease
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Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons
Globally, cardiovascular disease (CVD) is the primary cause of morbidity and mortality, accounting for more than 70% of all fatalities. Machine learning (ML) can be used to predict the risk of a heart attack. The algorithms used for this task would be supervised ML algorithms, such as Random Forest, Logistic Regression, Support Vector Machines, etc.…
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Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)
The project utilizes AI-driven cardiovascular medicine with a focus on early diagnosis of heart disease using Artificial Neural Networks (ANN). Aiming to improve early detection of heart issues, the project processed a dataset of 303 patients using Python libraries and conducted extensive exploratory data analysis. A Sequential ANN model was subsequently built, revealing excellent performance…
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ECG Early Warning System (EWS) in Terms of Time-Variant Deformations and Creep-Recovery Strain Tests
Featured Photo by Hernan Pauccara on Pexels Referring to an earlier stress-strain case study, the objective of this risk management project is to develop the ECG Early Warning System (EWS) based upon time-dependent viscoelastic deformations and observed creep-recovery mechanisms in the cardiac muscle. The creep-recovery test involves loading a material at constant stress, holding that…
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Heart Failure Prediction using Supervised ML/AI Technique
Introduction This project is aimed to support ESC guidelines [1] that help health professionals manage people with heart failure (HF) according to the best available evidence. The objective is not only to develop an accurate survival prediction model but also to discover essential factors for the survival prediction of HF patients. The complex nature of HF produces a significant amount…
