Early Heart Disease Prediction Using GAN-Augmented Data and Machine Learning

Cardio-Vascular Diseases (CVD) are found to be a significant factor in the populace leading to fatal death. This study aimed to create a smart system to predict heart disease early on. We used advanced computer techniques to analyze patient data and identify patterns linked to heart problems. By carefully selecting the most important information and using a method called Generative Adversarial Network (GAN)s to generate additional realistic patient data, we improved the accuracy of our predictions. The study uses 890 records gathered from Al Razi hospital in city of Jenin. Because the sample size was insufficient to get accurate prediction, it was increased by using the Generative Adversarial Network (GAN) Algorithm. The performance of the proposed ML model was estimated using numerous ML algorithm. K-Nearest Neighbors (KNN), Random Forest, AdaBoost, and Support Vector Machines (SVM) were used in the model. These methods helped us better understand the data and make more accurate predictions. The results obtained using this approach achieve a 99% accuracy rate by using KNN and SVM models and utilizing GAN-generated samples and feature selection strategies. These findings show that the approach that combines feature selection and machine learning algorithms is useful for the early and accurate prediction of heart disease.