ANALISIS DAN PERBANDINGAN PERFORMA KINERJA METODE DECISION TREE DAN KNN DALAM MEMPREDIKSI RISIKO PENYAKIT KARDIOVASKULAR
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Abstract
Cardiovascular disease (CVD) is the leading cause of death globally today. CVD risk prediction is important for prevention and early treatment. Machine learning methods such as Decision Tree and K-Nearest Neighbors (KNN) have been used to predict CVD risk. This study evaluates the performance of both machine learning methods in predicting CVD risk based on a dataset that includes important variables such as age, gender, blood pressure, and cholesterol levels. The results of this analysis show that the performance of Decision Trree and KNN varies depending on the dataset and parameters used. Decision Tree tends to provide more accurate results in some scenarios, while KNN excels in other situations. However, both methods have their own advantages and disadvantages, which need to be considered in the context of clinical applications. These findings provide important insights for healthcare practitioners in choosing the best method to predict CVD disease risk. The use of machine learning methods can improve early diagnosis an effective intervention in treating CVD. In conclusion, a better understanding of the performance of Decision Tree and KNN can help improve the management of CVD risk more effectively and efficiently.
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