| Autor: |
Jyothirmayi, P., Thomas, A., Karpagam, V., Deepak, A., Prathibhaa, R., Yong, L. C. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3161 Issue 1, p1-7, 7p |
| Abstrakt: |
The primary objective of this study is to utilize the Decision Tree (D-Tree) classifier for the purpose of diagnosing cardiac disease in contrast to the Naive Bayes (NB) model. Dataset employed in proposed investigation was obtained from the UCI. machine learning repository. Analysis was conducted by means of G-power 0.8, wherein the values of alpha and beta were set at 0.05, 0.2, respectively, alongside a confidence interval of 95%. The sample size utilized for the diagnosis of heart disease consisted of 40 samples, with 20 in Group 1 and 20 in Group 2. Both the Decision Tree (D- Tree) classifier and the Naive_Bayes (NB) model were employed to identify heart disorders, each with a sample size of 20. It was determined that the Decision Tree (D-Tree) classifier exhibited an accuracy rate of 90.43%, surpassing that of the Naive_Bayes (NB) model, that achieved an accuracy value of 78.56%. This investigation uncovered a significance level of 0.001, indicating a statistically compromised difference between the test values of samples groups (p<0.05). When compared to the Naive_Bayes (NB) model's accuracy value of 78.56%, the Decision Tree (D-Tree) classifier demonstrated superior performance with an accuracy value of 90.43% in the recognition of cardiac disorders. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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