Implementasi Algoritma Logistic Regression pada Pembuatan Website Sederhana untuk Prediksi Penyakit Jantung - Chyntia Raras Ajeng Widiawati; Lisa Nurazizah; Ika Romadoni Yunita (P3M Politeknik Negeri Cilacap, 2024)
Heart disease is a deadly disease, early recognition is important to prevent the fairly high death rate due to this disease. There are various ways to detect heart disease early, one of which is by utilizing machine learning. In this research, the author uses secondary data, namely data taken from the website www.kaggle.com for the prediction process. The amount of data used was 297 data, with details of 160 data not detecting heart disease, and 137 data detecting heart disease. Apart from making predictions from heart disease patient data using the logistic regression algorithm, this research also implements the model that has been created on the website. The results of implementing the logistic regression algorithm in this research are an accuracy value of 0.9, precision of 0.92, recall of 0.86, and f1-score of 0.89. After measuring using these 4 parameters, the model that has been created is then implemented into a simple website using the Rapid Application Development (RAD) method.