Penerapan Algoritma K-Nearest Neighbor (K-NN) untuk Analisis dan Prediksi Dini Penyakit Stroke
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Abstract
Stroke is one of the leading causes of death and long-term disability worldwide, making early detection efforts based on data analysis essential to minimize the associated risks. This study applies the K-Nearest Neighbor (K-NN) algorithm as a classification method to predict the likelihood of stroke occurrence based on medical and lifestyle attributes. The research stages include data preprocessing, normalization, Euclidean distance calculation between training and testing data, selection of the optimal K value, and classification based on the nearest neighbors. The dataset was divided using a ratio of 0.9 for training data and 0.1 for testing data. The experimental results show that the K-NN algorithm achieved an accuracy of 60% in predicting stroke risk. Although the obtained accuracy is categorized as moderate, the results indicate that K-NN is capable of capturing basic patterns within a heterogeneous stroke dataset. With further optimization of parameters and improved preprocessing techniques, the K-NN algorithm has the potential to be developed as a supportive method for data-driven early stroke detection systems in digital health applications.
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