CardioNet.AI - Heart Disease Predictor Model

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, with an estimated 20.5 million deaths in 2021, predominantly in low- and middle-income countries. Early detection is critical to reducing this burden, yet traditional diagnostic methods are often resource-intensive. This study introduces CardioNet.AI, a neural network-based model designed to predict heart disease using the UCI Heart Disease dataset, comprising millions patient records with 14 clinical attributes. The model employs a feedforward neural network with two hidden layers, achieving an accuracy of 81.97% on the test set for binary classification (disease vs. no disease). Notably, it demonstrates a high recall of 94% for identifying patients with heart disease, making it a promising tool for clinical screening. CardioNet.AI’s straightforward architecture enhances its potential for integration into healthcare systems, particularly in resource-constrained settings, contributing to global efforts to combat CVDs.