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

被引:3
作者
Cocianu, Catalina-Lucia [1 ]
Uscatu, Cristian Razvan [1 ]
Kofidis, Konstantinos [1 ]
Muraru, Sorin [1 ]
Vaduva, Alin Gabriel [1 ]
机构
[1] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 010552, Romania
关键词
evolution strategies; tree-structured Parzen estimator (TPE); LSTM neural networks; SVM classification; data preprocessing; random forest; KNN; logistic regression;
D O I
10.3390/electronics12071663
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:21
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