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Journal of Advanced Health Informatics Research
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856124     DOI : https://doi.org/10.59247/jahir.v1i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health
Journal of Advanced Health Informatics Research (JAHIR) is a scientific journal that focuses on the application of computer science to the health field. JAHIR is a peer-reviewed open-access journal that is published three times a year (April, August and December). The scientific journal is published by Peneliti Teknologi Teknik Indonesia (PTTI). The JAHIR aims to provide a national and international forum for academics, researchers, and professionals to share their ideas on all topics related to Informatics in Healthcare Research
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2023)" : 5 Documents clear
Understanding the Perception of Adolescent Challenges in Body Mass Index Reduction: A Qualitative Study Suwarsi; Joshepine Lorica; Hastuti, Agustina Sri Oktri
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.v1i3.39

Abstract

A weight loss program for adolescents needs to be done immediately, considering that the number of adolescents who are obese is increasing. This research contributes to identifying the challenges faced by overweight adolescents in weight loss programs. Data was collected by filling out a questionnaire given directly to the participants. The location where the research was conducted at Yogyakarta City, Indonesia. The questionnaire has been tested for validity and reliability with a value of more than 0.85. This study uses a qualitative research design, with a sample of adolescents aged 15-19 years who are obese. The number of participants is 15; according to the inclusion criteria, the data is taken through in-depth interviews. Results: The difficulty of overweight adolescents in losing weight is due to the lack of parental support in serving food and the lack of support from friends in diet programs. Health programs for adolescents, especially weight loss programs, need to involve peers and support from parents
Artificial Intelligence-Based Mobile Health Solutions in the Health 4.0 Era Ariefah Khairina Islahati; Purwono, Purwono; Bala Putra Dewa
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.v1i3.166

Abstract

In the Health 4.0 era, technological advances continue to bring major changes to the health care industry. Artificial intelligence (AI)-based Mobile Health (mHealth) solutions are an important innovation that will meet modern needs. As time goes by, modern society is increasingly dependent on health technology in everyday life, especially to manage their health conditions. The use of Mobile Health, especially via smartphone devices, has been proven to provide more personalized and affordable health services. Additionally, AI makes diagnosis and health monitoring easier. In this article, the concept of mHealth AI 4.0 is discussed, with particular emphasis on its critical role in providing responsive, proactive, and patient-focused healthcare. In the Health 4.0 era, it is hoped that we can make a positive contribution to improving the quality of health services by using this solution
Challenges and Opportunities: Integration of Data Science in Cancer Research Through A Literature Review Approach Purwono, Purwono; Ariefah Khairina Islahati; Yuslena Sari; Dewi Astria Faroek; Muhammad Baballe Ahmad
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.v1i3.167

Abstract

Several research articles in this journal relate to various aspects of cancer, such as treatment, patient outcomes, caregiver responsibilities, and the use of AI and liquid biopsy in cancer research. Covers a wide range of topics, including valuable insights into the latest developments in cancer research as well as potential future opportunities and issues. Several articles discuss the impact of non-coding RNA on gastric cancer, machine learning decision support systems for cancer survival factors, economic impact of cancer mortality, nausea in children diagnosed with cancer, protein-RNA variations in cancer clinical analysis, integration and proteomic data analysis in the context of cancer genomics, personalized cancer medicine, mass spectrometry-based clinical proteomics, cancer proteogenomics, subtype-based This journal provides an in-depth overview of various aspects of current cancer research and future research prospects
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
Exploration of Machine Learning Methods in Medical Disease Prediction: A Systematic Literature Review Ria Suci Nurhalizah; Hadi Jayusman; Purwatiningsih
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.v1i3.174

Abstract

Exploration of Machine Learning methods in the systematic literature shows successful applications in disease diagnosis, disease prediction, and treatment planning. This literature only includes discussions on Classification methods consisting of Support Vector Machine(SVM), Naïve Bayes, Nearest Neighbors and Neural Network(NN) and Regression consisting of Decision Tree, Linear Regression, Random Forest Ensemble Methods, and Neural Network(NN). Clustering which consists of K-Means Clustering, Artificial Neural Network (ANN), Gaussian Mixture, Neural Network (NN) and Dimensionality reduction which consists of Principal Component Analysis (PCA). In the context of healthcare, the importance of sustainability, ethics, and data security are key factors. This research uses Systematic Literature Review (SLR) to explore Machine Learning methods in the medical context and recommends Support Vector Machine, Random Forest, and Neural Networks as effective methods. By exploring 300 papers and selecting 57 papers for discussion of machine learning methods in medical disease prediction. Method selection should be tailored to the dataset characteristics and disease prediction goals, while prioritizing

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