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All Journal Sistemasi: Jurnal Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Infotekmesin Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Prosiding University Research Colloquium
Saputri, Inka
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Infotekmesin

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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 Arsi, Primandani Dhanar Intan Surya Saputra Emi Nurlaela Febrianti, Diah Ratna Irma Darmayanti Isnaini, Khairunnisak Nur Mustofa, Dinar Nandang Hermanto Nurul Hidayati Raras Ajeng Widiawati, Chyntia Rujianto Eko Saputro Sarmini , Sarmini Sarmini Sarmini Tarwoto Yunita, Ika Romadoni