cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ronal Watrianthos
Contact Email
ronal.watrianthos@gmail.com
Phone
+6281263621335
Journal Mail Official
joseitjournal@gmail.com
Editorial Address
Professional Organization - Ikatan Ahli Informatika Indonesia (IAII) / Indonesian Informatics Experts Association Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu 12540 South Jakarta - Indonesia http://iaii.or.id/
Location
Unknown,
Unknown
INDONESIA
Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 41 Documents
 1   2   3   4   5 
An Enhanced Comprehensive Study Towards Predicting Cardiovascular Disease Using Machine Learning Techniques. Anil Kumar Prajapati
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 3 (2026)
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i3.7585

Abstract

. In recent years, heart and cardiovascular diseases have become more common, causing a significant increase in mortality rates worldwide. Data from various organisations highlights the severity of heart disease, which remains a major concern. Accurately and quickly identifying severe conditions, such as heart disease, is vital for effective prevention. Techniques such as data mining, machine learning, and deep learning have been used in medicine to reliably detect heart disease. However, these methods depend on data that can change over time. To ensure accurate detection, proper use of historical data is essential; otherwise, results can be inaccurate. Machine learning techniques produce outcomes based on mathematical calculations, so data cleaning and refinement are necessary. Disease-related data can include text, numbers, and images, which may vary widely, requiring extensive stratification, normalisation, cleaning, encoding, and randomisation; otherwise, results may be biased. Our previous review article addressed a specific challenge related to the CVD Prediction Model. This Enhanced review primarily examines how machine learning techniques operate on medical datasets and their effectiveness in predicting cardiovascular diseases (CVD). It also aims to analyse datasets, features, and machine learning methods used in CVD prediction

Page 5 of 5 | Total Record : 41

 1   2   3   4   5 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol 3 No 3 (2026) Vol 3 No 2 (2025) Vol 3 No 1 (2025) Vol 2 No 3 (2024) Vol 2 No 2 (2024) Vol 2 No 1 (2023) Vol 1 No 3 (2023) Vol 1 No 2 (2022) Vol 1 No 1 (2022)