Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models
Hossein Sadr, Arsalan Salari, Mohammad Taghi Ashoobi, Mojdeh Nazari
Abstract
BACKGROUND: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities. OBJECTIVE: The goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning. METHOD: Two public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class. RESULT: The proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease.