Comparative Analysis of Random Forest and SVM Performance in Asthma Prediction
DOI:
10.33395/sinkron.v9i1.14346Keywords:
Asthma Prediction, Error Rate, Forecasting Time, Medical Datasets, Performance Comparison, Prediction Capabilities, Random Forest, Support Vector Machine, Testing Time, Training TimeAbstract
This study evaluates the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in predicting asthma risk to identify the most suitable method for medical datasets. Key metrics include training time, testing time, forecasting time, error rate, and accuracy. The datasets involve attributes such as age and clinical factors, analyzed in three stages: training, testing, and forecasting.
During training, SVM demonstrated faster processing, requiring a maximum of 0.5323 seconds compared to RF's 3.7736 seconds on 90% training data. In testing, RF excelled in speed, achieving a minimum testing time of 0.0215 seconds, while SVM required 0.0984 seconds on 10% test data. However, SVM consistently outperformed RF in error rate, with a minimum error rate of 19.17%, compared to RF's 25.10%.
During training, SVM demonstrated faster processing, requiring a maximum of 0.5323 seconds compared to RF's 3.7736 seconds on 90% training data. In testing, RF excelled in speed, achieving a minimum testing time of 0.0215 seconds, while SVM required 0.0984 seconds on 10% test data. However, SVM consistently outperformed RF in error rate, with a minimum error rate of 19.17%, compared to RF's 25.10%.
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