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All Journal Primary: Jurnal Pendidikan Guru Sekolah Dasar Pendas : Jurnah Ilmiah Pendidikan Dasar Bioscientia Medicina : Journal of Biomedicine and Translational Research Humantech : Jurnal Ilmiah Multidisiplin Indonesia MUSHAF JOURNAL: Jurnal Ilmu Al Quran dan Hadis Jurnal Ilmu Pendidikan dan Kearifan Lokal (JIPKL) Journal of Educational Management and Strategy Journal of Educational Management Research JIP: Jurnal Ilmu Pendidikan IJLHE: International Journal of Language, Humanities, and Education Jurnal Ilmiah Research Student Journal of Social Science and Economics Jurnal Komunitas Farmasi Nasional Jurnal Multidisiplin Indonesia Indonesian Journal of Education Fortunate Business Review Journal of Education and Science (EDUSAINS) Data Science Insights Jurnal Farmasi Ikifa
Charles
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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Journal : Data Science Insights

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Prediction of Heart Failure in Patients using Five Types of Algorithms Charles
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.129

Abstract

Heart failure is one of the cardiovascular diseases that has a significant impact on patients' quality of life and requires appropriate medical treatment. With the advancement of technology, the use of machine learning algorithms to predict the risk of heart failure can enhance the efficiency of diagnosis and treatment. This study aims to compare the performance of five machine learning algorithms in predicting heart failure in patients. The algorithms used in this study are K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Deep Learning. The dataset contains patients' medical data, including medical history, symptoms, and clinical test results. The evaluation method was carried out by measuring the accuracy, precision, recall, and F1-score of each algorithm. The results show that the Random Forest algorithm achieved the best performance in terms of accuracy and prediction stability, followed by Deep Learning and Naïve Bayes.
Co-Authors Agustina, Shela Aries Meryta Meryta Arman Husni Astri Zufitasari Auroka, Enjelica Azizatul Afifah Beckham, Felix Cynthia Dewi Sinardja Dharma, Ernest Kusuma Elmida Yenti Febi Febrianda Febri Janatul Yuda Firza Aulani Gema Hista Medika Guruh Subagya Hanif Cahyo Adi Kistoro Hasifa Hayati, Mufidah Herty Nur Tanty Herty Nur Tanty Indhira Asih Vivi Y Lianitami Atmawati Lie, Eric Makaba, Kharisma Austin Margareta, Siska Masitoh Aini Meiana Dwi Andini Michael Michelle Muhammad Fauzan Muhammad Hafizh Muhiddinur Kamal Nasution, Soibatul Aslamiah Nia Rahmanita Andria Nurhasnawati Poh, Stella Restu Saputra Rina Yuliana Romi Febrianto Saripuddin Napitupulu Selly Septiana, Yulda Dina Sherlyna Siti Aisyah St. Erfi Yulvira Tee Pei Chi, Charity Tioputri, Alexa Wedra Aprison Wildan Wulan Say Yarhami Fadillah Yesi Ulandari Yuherman Yusutria Yusutria Yuzarion, Yuzarion Zakir, Supratman Zulmi, Fadhli