Web-Based Heart Disease Prediction System Using SVM Method and Streamlit Framework
Sistem Prediksi Penyakit Jantung Berbasis Web Menggunakan Metode SVM dan Framework Streamlit
DOI:
https://doi.org/10.21070/ups.1378Keywords:
Classification, Python, Machine Learning, Support Vector MachineAbstract
Heart disease is a disease whose cases often occur in society regardless of age, gender and lifestyle. Heart attacks that are handled too late can cause dangerous complications with the most fatal risk, namely death. One of the machine learning algorithms that can be used is the Support Vector Machine. SVM can be used for regression and classification cases. The advantage of this algorithm is its fast computation. Classification of heart disease with the SVM algorithm results in an accuracy of 85%. The machine learning model that produces the accuracy value is then carried out by deploying the model using the Streamlit framework. This framework was created to make it easier for developers to build web-based programs in the fields of interactive data science and machine learning. The results of this study are web-based programs that can diagnose heart disease with an accuracy value of 85%.
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