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All Journal JUITA : Jurnal Informatika Jurnal Pendidikan Kedokteran Indonesia: The Indonesian Journal of Medical Education
Fatimah, Vita Arfiana Nurul
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Computer Science & IT Health Professions Medicine & Pharmacology Public Health

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Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach Biddinika, Muhammad Kunta; Masitha, Alya; Herman, Herman; Fatimah, Vita Arfiana Nurul
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.24153

Abstract

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.
Innovative Learning Approaches for Medical Students: A Comparative Analysis of Hybrid Learning vs Conventional Lectures Mayasari, Dyah Samti; Hafizhah, Bidhari; Abdullah, Hafidz; Hasana, Shofuro; Alfina, Saski Yasmin; Fatimah, Vita Arfiana Nurul; Solikhah, Hana Maryam; Kokasih, Orisativa; Gharini, Putrika Prastuti Ratna
Jurnal Pendidikan Kedokteran Indonesia: The Indonesian Journal of Medical Education Vol 14, No 4 (2025): December
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpki.106109

Abstract

Introduction: With the rapid progress of technology, learning approaches have increasingly moved online, including the method for learning electrocardiography (ECG). ECG interpretation is a core skill for physicians, especially in emergency settings. Massive Open Online Courses (MOOCs) provide a flexible and complementary learning method that complements traditional classroom instruction. This study aims to evaluate the effectiveness of MOOCs as a complementary learning method and compare that with conventional learning in enhancing ECG knowledge among second-year preclinical students.Methods: A prospective cross-sectional study with consecutive sampling was conducted to recruit participants. They were divided into a control group (conventional lectures) and an intervention group (using MOOC as a complementary method). All participants are required to complete pre-test and post-test questionnaires, as well as evaluations after each module. This study compared learning gain scores between traditional and hybrid learning methods.Results: Of the 258 participants registered, 160 students completed the learning modules, including the post-test. The majority of participants were female, all under 25 years old, and had been in medical education for 1.5 to 2 years. Overall, the gain score achieved was 2.03 for traditional and 2.75 for hybrid. While the topics of electrolyte imbalance and heart enlargement showed increasing scores, arrhythmia and ECG in ischemia and infarction showed lower scores for the hybrid method. Not all registered participants completed the course; the main factor motivating participants to complete the course was gaining knowledge (80.00%). Conclusion: Hybrid ECG learning using online media, such as video lectures and mini-quizzes, improves interpretation skills more effectively than traditional methods by boosting engagement and knowledge retention. To further enhance learning, integrating artificial intelligence-driven reminders and monitoring can improve completion rates, and continuous updates to the curriculum are necessary to strengthen medical students' ECG competence.
Co-Authors Abdullah, Hafidz Alfina, Saski Yasmin Gharini, Putrika Prastuti Ratna Hafizhah, Bidhari Hasana, Shofuro Kokasih, Orisativa Mayasari, Dyah Samti Muhammad Kunta Biddinika Solikhah, Hana Maryam Wiwik Handayani