Heart Disease Prediction Redefined: Hybrid CNN & MobileNet Model Comparison

As reported by the World Health Assembly , heart problems, the majority of mortality globally, accounted for around 17.9 million cases in 2019, or over 32% of death rates worldwide. The need for early heart illness detection drives research into establishing effective machine learning-based diagnostic systems. At the present, the growth of machine learning in healthcare largely supports the ability of analysts or physicians to carry out their jobs, spot healthcare trends, and generate illness prediction models. Deep learning, on the other hand, has advanced quickly and emerged as the most widely used technique in recent years, including for illness detection. Optimizing A combination of convolutional neural network (CNN) as well as Mobilenet approach for cardiac disease classification is the major objective of this project. Through utilizing sophisticated DL methods such as LSTM, SVC+Rf as Hybrid model and CNN+Mobilenet Algorithm, we were able to attain an impressive testing accuracy that reached 98.54% CNN+Mobilenet By integrating the structured datasets with modern DL algorithms, this work unveils a unique solution to the challenge of expandable and accurate discoveries of the heart sickness, resulting in the power to enhance the clinical results while decreasing death rates.