Key result
The K-Nearest Neighbor (K=1) classifier achieved the highest classification accuracy of 99.7% for predicting heart disease, which improved to 100% after feature extraction using only 4 attributes.
Why the study?
Heart disease is common, making early diagnosis crucial to prevent disease and save lives, motivating comparative analysis of classifiers to predict cases with minimal attributes.
Machine learning classifiers, particularly K-NN, can achieve high accuracy in predicting heart disease using a minimal number of selected attributes.
Machine learning classifiers may support HD prediction using few attributes; leaves open prospective validation before clinical use.
BACKGROUND: Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases. RESULTS: It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases. CONCLUSION: Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.
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Khaled Mohamad Almustafa (2020) studied Heart disease (n=1,025). Machine learning classifiers (K-NN, Decision tree J48, JRip) vs. Other classifiers (Naive Bayes, SGD, SVM, Adaboost, Decision Table) was evaluated on Classification accuracy. The K-Nearest Neighbor (K=1) classifier achieved the highest classification accuracy of 99.7% for predicting heart disease, which improved to 100% after feature extraction using only 4 attributes.
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