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Raras Ajeng Widiawati, Chyntia
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Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Implementasi Algoritma Logistic Regression pada Pembuatan Website Sederhana untuk Prediksi Penyakit Jantung Raras Ajeng Widiawati, Chyntia; Nurazizah, Lisa; Yunita, Ika Romadoni
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2048

Abstract

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.
Pengembangan Model Machine Learning Berbasis Linear Discriminant Analysis (LDA) untuk Deteksi Gejala Penyakit Jantung Menggunakan Python Saputri, Inka; Raras Ajeng Widiawati, Chyntia; Sarmini , Sarmini; Yunita, Ika Romadoni
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2377

Abstract

Heart disease is the leading cause of death globally and is often not detected early due to limited awareness and the high cost of medical diagnosis. This study aims to develop an accurate and efficient prediction model for heart disease using the Linear Discriminant Analysis (LDA) algorithm. The dataset, obtained from Kaggle, contains 1,024 patient records with 14 clinical attributes, including age, blood pressure, cholesterol, and ECG results. The preprocessing steps include handling outliers, duplicates, class imbalance using SMOTE, and feature standardization. The model was evaluated using cross-validation and learning curve analysis. Results show that the optimized LDA model, tuned with GridSearchCV, achieved an accuracy of 82.54%, a recall of 88.91%, a precision of 79.03%, and an F1-score of 83.54%. The model demonstrates balanced and stable performance, although some misclassification in the positive class remains. This study highlights LDA as a promising method for the early detection of heart disease based on structured clinical data.
Co-Authors Nurazizah, Lisa Saputri, Inka Sarmini , Sarmini Yunita, Ika Romadoni