Cardiac Disease Prediction with Tabular Neural Network

Cardiac disease, which includes a variety of diseases that affect the heart, is a leading cause of death worldwide. One of every four deaths in the United States is due to heart disease. This means that approximately 610,000 people die from the disease each year. Heart disease is much easier to treat if detected early. Machine learning can play a crucial role in early detection and save lives. This research aims to develop an artificial intelligence-based system that identifies patients who are more likely to develop heart disease based on their medical history. The heart disease dataset from the UCI Machine Learning Repository was used for training and validation. Traditional classification techniques such as logistic regression, random forest, gradient boosting, and extreme gradient boosting were used as base models, and the results were compared with the Tabnet model. Tabnet is a new robust, interpretable, deep-learning architecture for tabular data. TabNet uses sequential attention to choose which features to conclude from at each decision step, focusing its learning ability on the most salient features, allowing for interpretability and more efficient learning. Promising results were obtained and validated using ROC curves, accuracy, precision, sensitivity, specificity, and confusion matrices. The Tabnet deep learning model outperformed the others, achieving 94% accuracy, ROC score of 0.94, and specificity and sensitivity greater than 0.93