Sistem Prediksi Penyakit Jantung Berbasis Web Menggunakan Metode SVM dan Framework Streamlit

Ary Putranto(1*), Nuril Lutvi Azizah(2), Ika Ratna Indra Astutik(3),

(1) Universitas Muhammadiyah Sidoarjo, Indonesia
(2) Universitas Muhammadiyah Sidoarjo, Indonesia
(3) Universitas Muhammadiyah Sidoarjo, Indonesia
(*) Corresponding Author

Abstract


Heart disease is a disease whose cases often occur in society regardless of age, gender and lifestyle. Most of these diseases can not be cured completely. Heart attacks that are handled too late can cause dangerous complications with the most fatal risk, namely death. With the rapid development of technology at this time, the world of medicine is very helpful. One of the technological advances that exist today is a system that can diagnose disease. 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. Streamlit is a Python-based framework that is open source. 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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References


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DOI: https://doi.org/10.30645/kesatria.v4i2.180

DOI (PDF): https://doi.org/10.30645/kesatria.v4i2.180.g179

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