Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Klasifikasi Resiko Penyakit Jantung
-
Aprillia Wulan Nanda Dari
*
Universitas Amikom Yogyakarta, Yogyakarta,
Indonesia
- Ika Nur Fajri Universitas Amikom Yogyakarta, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
Heart disease is one of the deadliest diseases in the world, where there is a disruption in the function of the heart and blood vessels that causes chest pain, irregular heartbeat, and difficulty breathing. According to data from the World Health Organization (WHO), there are 17.9 million deaths each year due to heart disease. The difficulty in classifying heart disease accurately and quickly is a significant problem. From this problem, researchers conducted data mining research using the KNN algorithm to classify the risk of heart disease by taking data from the official Kaggle website. In this study, there are 4 stages, namely data collection, model formation, mode evaluation, and prediction interface. By using the KNN algorithm, the analysis results obtained an accuracy of 83%, precision 0.88, recall 0.77 and f1-score 0.82. With the results of the model evaluation data, it shows that the classification of heart disease risk using the KNN algorithm has quite good performance. The results of the modeling are then presented in the form of a website by deploying the model.
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References
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