| Autor: |
Kumar, S. V., Kalaiselvi, K., Selvaperumal, S. K., Lakshamanan, R. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3161 Issue 1, p1-6, 6p |
| Abstrakt: |
The objective of this study is to improve the accurateness of the skin disease prediction using ML algorithm by comparing SVM and Extreme Gradient Boosting (XGBOOST) models. Skin disease prediction models developed using ML method have improved the accuracy of diagnosis. The methods currently available for building accurate skin disease prediction models require extensive data collection. This study compared the enactment of two popular ML algorithms, SVM and XGBOOST, in predicting skin disease using a dataset obtained from Kaggle consisting of clinical and demographic variables. The dataset was preprocessed, and both algorithms trained and tested both algorithms and found that XGBOOST outperformed SVM. The findings of this study indicate that machine learning algorithms have the capability to provide precise predictions of Skin disease. The outcomes of the autonomous samples t-test indicated that the mean accurateness of XGBoost was expressively higher than the mean accuracy of SVM (p < 0.001, p < 0.05), a statistically noteworthy variation between the algorithms related to classification accuracy. By this learning accurateness of skin disease prediction is improved while analyzing the results of two ML algorithms SVM and XGBoost. The study compares the accuracy of both algorithms using a dataset. The conclusion is that the proposed model of XGBOOST 94% outperformed the SVM 59.6% within this dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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