Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

“…The findings affirm that the amalgamation of machine learning and meta-heuristic algorithms leads to superior classification accuracy with a reduced number of features. Hybrid methodologies that integrate hyper-parameter optimization algorithms with two highly effective classification techniques namely: Support Vector Machines (SVMs) and Long Short-Term Memory (LSTM) neural networks have been proposed in [85] to further improve the accuracy of cardiovascular disease diagnosing. The results were achieved based on the Cleveland dataset and its extension Statlog.…”

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

“…Technological improvement in computers and other sciences has led to amazing advances in the gathering and storage of data of various forms [ 1 , 2 ]. For the purpose of keeping data about business transactions, agriculture, traffic, the Internet, astronomy, phone calls, and other subjects, large databases are required [ [3] , [4] , [5] ].…”

Multiobjective Optimization of Fuzzy System for Cardiovascular Risk Classification

“…The use of artificial intelligence and optimization techniques has shown promising results in various healthcare applications [17][18][19][20][21][22][23][24][25][26][27], including the management of CVDs and related risk factors, offering a valuable tool for improving public health. For example, artificial neural networks (ANNs) have been used to elucidate the connections between olfaction, eating habits, and metabolic disturbances in the health of overweight patients [17].…”

“…Machine learning techniques have also been used for early the prediction and classification of CVD [22,23], as well as for the explicable detection of COVID-19 using chest radiographs [24]. In addition, studies have covered cardiovascular risk factors in obesityrelated cardiovascular issues in Romanian children and adolescents using retrospective data analysis [25] and have examined the associations between multiple trajectories of BMI and waist circumference with blood pressure and hypertension in Chinese adults in a prospective study [26].…”