Machine learning methods including Decision Tree, Support Vector Machine, and Stochastic Gradient Descent achieved 87.69% accuracy in identifying the presence of heart disease.
Can machine learning methods accurately identify the presence of heart disease based on specific features?
Machine learning algorithms, specifically Decision Tree, SVM, and SGD, can identify the presence of heart disease with an accuracy of 87.69% using specific features.
Currently, heart diseases are prevalent in the population. Machine learning methods may help in the identification of heart diseases in the different people with the analysis of various features of heart rate, such as PPE, spread, spread2, MDVP:Fo(Hz), MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5, MDVP:APQ, Shimmer:DDA, DFA, RPDE, D2, MDVP:Fhi(Hz), MDVP:Flo(Hz), NHR, HNR, MDVP:Jitter(Abs), MDVP:Jitter(%), MDVP:RAP, MDVP:PPQ, and Jitter:DDP. The analysis was performed with the dataset from the UCI Machine learning repository from the Center for Machine Learning and Intelligent Systems. This paper proposes the use of different methods, such as Neural Network, Decision Tree, k-Nearest Neighbor (kNN), Combined nomenclature (CN2) rule inducer, Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). The best results on the 20-fold Cross-validation and the 10-fold Cross-validation are reported by DT and SVM methods (87.69%). Also, the best results on the 5-fold Cross-validation are reported by SGD (87.69%).
Pires et al. (Wed,) conducted a other in Heart disease. Machine learning methods (Neural Network, Decision Tree, kNN, CN2, SVM, SGD) was evaluated on Identification of heart disease (model accuracy). Machine learning methods including Decision Tree, Support Vector Machine, and Stochastic Gradient Descent achieved 87.69% accuracy in identifying the presence of heart disease.
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