Hybrid Deep Learning for Heart Disease Prediction: Fourier Transformation meets CNN on MIT-BIH Arrhythmia Dataset

heart disease continues to cause major morbidity and death globally. Heart disease prognosis and prevention remain difficult despite medical breakthroughs. Coronary artery disease, heart failure, arrhythmias, and congenital heart abnormalities are all types of heart disease. These illnesses strain people, healthcare systems, and economies worldwide. Age, gender, genetics, hypertension, diabetes, obesity, smoking, and inactivity are heart disease risk factors. Machine learning algorithms may improve illness prediction and risk stratification using vast medical data. ML models may find complicated patterns and interactions in electronic health records, medical imaging, genetic data, and lifestyle variables. Hybrid ML models increase predicted accuracy and generalizability by combining decision trees, support vector machines, neural networks, and ensemble approaches. Innovative prediction and preventative methods are needed to combat heart disease. Machine learning hybrid models may improve heart disease risk prediction by integrating varied data sources, using complicated algorithms, and boosting forecast accuracy.