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All Journal Journal of Advanced Health Informatics Research
Iis Setiawan Mangkunegara
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Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health

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The Use of Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review Lutviana; Iis Setiawan Mangkunegara
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.172

Abstract

This paper discusses the crucial role of Artificial Intelligence in improving disease diagnosis and the use of medical data in the era of big data. Through the Systematic Literature Review (SLR) approach, we review recent developments in the application of Machine Learning (ML) to disease diagnosis, evaluate the ML techniques applied, and highlight their impact on healthcare. BP-CapsNet uses the Convolutional Capsule Network (CapsNet) to diagnose cancer with advantages in overcoming invariance to image transformation. The Stacking Classifier achieves 92% accuracy in detecting heart defects with the advantage of combining weak learners. CraftNet, a combination of deep learning and handmade features, demonstrates strong recognition capabilities in cardiovascular disease. ML in infectious diseases demonstrates the ability to process big data, focusing on bacterial, viral, and tuberculosis infections. In heart disease diagnosis, ML, especially with CNN and DNN, detects disease at an early stage, despite the challenges of data imbalance. The ensemble algorithm for heart disease prediction demonstrates the superiority of categorical medical features, with SVM and AdaBoost as suitable methods. A new CNN for wide QRS complex tachycardia provides accurate results. In vestibular disease, five ML algorithms provide satisfactory results, with SVM as the best. These findings detail the development of ML in disease diagnosis, highlighting future challenges and opportunities in its use
Exploring Blockchain as a Security Framework for IoT in Healthcare: A Systematic Literature Review Lutviana; Iis Setiawan Mangkunegara; Hamzah M. Marhoon
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.290

Abstract

This systematic literature review explores the use of blockchain technology to improve the security of communication between IoT devices in the healthcare sector. The integration of IoT in healthcare has revolutionized patient data management but introduced security challenges. Blockchain, as a decentralized and immutable ledger, offers a potential solution by providing a secure method of data storage and transmission. This review analyzed 62 relevant studies published in the last five years using the PRISMA methodology. The main contributions of blockchain include improved data security, privacy, and data integrity, with decentralization and cryptographic techniques ensuring patient data remains secure and accessible only to authorized entities. Challenges of blockchain implementation include interoperability, data storage efficiency, and the need for strong cryptographic algorithms. Proposed solutions include the development of a specialized blockchain framework for healthcare, the integration of advanced encryption methods, and the use of distributed ledger technology to manage electronic medical records. Further research is needed to develop more efficient and secure blockchain solutions in healthcare applications, including improved interoperability, encryption algorithms, and real-world case studies. Although challenges remain, blockchain has great potential to improve the communication security of IoT devices in the healthcare sector.
Co-Authors Hamzah M. Marhoon Lutviana