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Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 55 Documents
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Optimising Cataract Detection in Fundus Images through EfficientNet-Based Classification Ibrahim, Andi; Sabara, Edi; Dirsam, Winarlin; Aziz, Faruq
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.25

Abstract

Turbidity of the lens of the eyeball that causes blindness or loss of vision is known as a cataract. By diagnosing the causes and symptoms of cataracts, early detection helps patients in prevention and treatment. The purpose of the research was to classify the image of the fundus into two classes: normal and cataract. The study also looked at how the optimizers for stochastic gradient descent, adaptive moment estimation, root mean square propagation, adaptive gradient algorithm, adaptive delta, and Nesterov-accelerated adaptive moment estimation stacked up against each other. We used the EfficientNet architecture in CNN and preprocessed the normal fundus and cataract fundus images by dividing each into training data (N = 80) and validation data (N = 20) from the Kaggle repository. We added test data from the normal fondus image (N =20) to see the accuracy of the results. We get 100% accuracy of training data, 87% and 77% validation data, and 100% and 95% test data.
Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique Irmawati, I; Herdit Juningsih, Eka; Yanto, Y
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.27

Abstract

This study presents a predictive modeling framework for osteoporosis risk assessment using ensemble techniques, specifically XGBoost and Bagging. Leveraging a dataset comprising comprehensive health factors influencing osteoporosis development, including demographic details, lifestyle choices, medical history, and bone health indicators, the aim is to facilitate accurate identification of individuals at risk. The dataset consists of 1958 samples, evenly distributed between osteoporosis-positive and osteoporosis-negative cases. The methodology involves the separation of features and labels, followed by data splitting into training and testing sets. XGBoost, a powerful gradient boosting algorithm, is employed as the base estimator within a Bagging ensemble, enhancing predictive accuracy and generalization. The model is trained on the training set and evaluated using cross-validation techniques to ensure robustness and mitigate overfitting. The results of the classification report demonstrate promising performance metrics, with an overall accuracy of 88% on the test set. Precision and recall scores indicate strong predictive capabilities, particularly in correctly identifying osteoporosis-positive cases. The novel integration of XGBoost within a Bagging ensemble provides an innovative approach to osteoporosis risk prediction, harnessing the strengths of both algorithms to improve model performance. This research contributes to the advancement of osteoporosis management and prevention strategies by providing a reliable tool for early risk assessment. The combination of machine learning techniques with comprehensive health data offers a valuable approach to personalized healthcare, enabling targeted interventions and optimized resource allocation. Ultimately, this study aims to enhance patient outcomes and reduce the burden of osteoporosis-related morbidity and mortality.
Efficient Skin Lesion Detection using YOLOv9 Network Faruq Aziz; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.30

Abstract

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection
Application of Deep Learning with ResNet50 for Early Detection of Melanoma Skin Cancer Khasanah, Nurul; Winnarto, Monikka Nur
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.31

Abstract

Cancer is a type of disease that can be fatal. Some of the cancers with the highest death rates in Indonesia include uterine cancer, breast cancer and skin cancer. The most malignant types of skin cancer are melanoma, which has a high mortality rate, especially if not detected in the early stages, and non-melanoma skin cancer (NMS Cs). Management of this disease depends on whether the type of skin cancer is malignant (malignant) or non-malignant (benign). Therefore, we need a system that can classify types of skin cancer with high accuracy. In this research, the author will use deep learning with the InceptionV3 and ResNet50 algorithms to carry out classification. The aim of this research is to classify types of skin cancer using the InceptionV3 and ResNet50 architecture. The skin cancer dataset used consists of two classes, namely Benign and Malignant, with a total of 3297 data, consisting of 660 data for testing and 2637 data for training. Research stages include data acquisition, preprocessing, classification, and analysis of results. Experimental results show that ResNet-50 produces the best performance with an accuracy level of 0.87. Innovations from this research include using a larger dataset, testing two deep learning architectures, modifying hyperparameters, and using a different layer architecture, which produces better accuracy than previous research. It is hoped that the results of this research can be applied to classify skin cancer more accurately.
Advances in Machine Learning and Deep Learning towards Medical Data Analysis Vebiyatama, Andicha; Ernawati, Muji
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.32

Abstract

Artificial intelligence uses advanced algorithms such as deep learning and machine learning methods to help doctors make more accurate diagnoses, identify potential health risks, and customize personalized treatment plans for patients. This literature review explores machine learning and deep learning methods applied to medical datasets over the past five years. The paper discusses the advancements, challenges, and future directions in utilizing ML and DL techniques for medical data analysis. It synthesizes recent research findings, highlighting key methodologies, datasets, and outcomes.
Implementing the Certainty Factor Method in a Dental Disease Expert System Gunawan, Andre; Islami, Fajrul
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.33

Abstract

Health, including dental and oral health, is a fundamental human necessity often overlooked by many. Dental and oral diseases can affect anyone unexpectedly, underscoring the critical need for timely professional advice from dentists based on symptoms presented by patients. Various factors contribute to the reluctance in seeking dental consultations, including inadequate awareness of oral health importance, financial constraints, and discomfort from lengthy wait times. This research proposes an Expert System utilizing the Certainty Factor method to diagnose dental diseases efficiently. The Certainty Factor method quantifies the certainty or uncertainty of facts within expert systems. Through computational analysis of multiple symptoms, the system accurately diagnosed a patient with Tooth Faktur disease with a high certainty level of 98.8%. Such an expert system promises to significantly aid dental professionals in diagnosing diseases promptly, facilitating appropriate treatment interventions.
Renewable Therapy Potential of Allogeneic Bone Marrow-Derived Mesenchymal Stem Cells for Idiopathic Pulmonary Fibrosis Bawono, Aloysius Krishartadi Damar; Balqis, Gasela Zalianti; Haq, Rais Amaral; Sari, Ratna Dewi Puspita; Utama, Winda Trijayanthi; Daulay, Suryani Agustina
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.34

Abstract

Idiopathic pulmonary fibrosis (IPF) is a rare chronic respiratory disease characterized by progressive fibrotic changes in lung tissue of unknown origin, resulting in severe decline in lung function and poor prognosis with a median survival of 3 to 5 years. Current pharmacological therapies, including nintedanib and pirfenidone, aim to slow disease progression but are limited by side effects and lack of efficacy in reversing established fibrosis. This literature review explores emerging therapeutic approaches for IPF using data from PubMed, Google Scholar, and ScienceDirect databases. The review highlights mesenchymal stem cell (MSC) therapy, specifically allogeneic bone marrow-derived MSCs, as a promising option. MSC therapy demonstrates superior efficacy in improving forced vital capacity (FVC) by 3.7%, surpassing the effects of nintedanib (3.3%) and pirfenidone (-4.8%), while exhibiting minimal adverse effects. The findings underscore the potential of MSC therapy as a renewable treatment option for IPF, suggesting a paradigm shift towards addressing both disease progression and lung function restoration in affected individuals.
Assessing the Validity and Reliability of a Questionnaire for Evaluating Pharmaceutical Services at a Hospital Utami Putri Siregar, Giel; Rizka Amanda, Laura; Dirsam, Winarlin; Sari Dewi, Ratna
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.35

Abstract

Pharmaceutical services are vital components of the healthcare system that aim to ensure the appropriate, safe, and effective use of medicines. This study aims to develop and test the validity and reliability of a questionnaire for assessing the quality of pharmaceutical services at a hospital in Jakarta. The questionnaire was designed based on five service quality dimensions: tangibles, reliability, assurance, empathy, and responsiveness. An initial set of 23 statement items was tested for content validity by calculating the Content Validity Ratio (CVR) and Content Validity Index (CVI). Several items were eliminated for not meeting the validity threshold. Subsequently, construct validity was tested by calculating Pearson product-moment correlations on data from 100 respondents who met the inclusion criteria, resulting in 13 valid statement items with r > 0.148. Reliability tests indicated Cronbach’s Alpha values above 0.6 for all dimensions, demonstrating good internal consistency of the questionnaire. Hypothesis testing results showed that all statement items had a significant relationship with the total questionnaire score (p < 0.05). Therefore, the questionnaire is valid and reliable for evaluating the quality of pharmaceutical services at a hospital in Jakarta.
Effects of Diet and Physical Activity on Coronary Heart Disease Risk Among Badminton Players Priyono, Eko; Ma'mun, Sukrul
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.36

Abstract

Coronary heart disease is caused by the narrowing or blockage of coronary arteries due to the buildup of fat and cholesterol, which hinders blood flow to the heart. This study aims to determine the relationship between dietary patterns and physical activity with coronary heart disease in the badminton player community in Indonesia. This research uses a cross-sectional design. The sample was taken using accidental sampling, involving 100 badminton players from various clubs in Indonesia. Data were collected using a questionnaire on June 24-27, 2024, and analyzed with SPSS using the chi-square test. The results showed that 100% of respondents did not have coronary heart disease, 64% often consumed carbohydrates, 71% often consumed protein, 71% rarely consumed fat, 56% rarely consumed fiber, 73% rarely consumed cholesterol, and 79% had heavy physical activity. The chi-square test showed a significant relationship between heavy physical activity and a family history of coronary heart disease (p-value = 0.036) and a nearly significant relationship between fat consumption and a family history of coronary heart disease (p-value = 0.066). The odds ratio showed a significant value (p-value = 0.019). These results indicate that there may be a relationship between the variables tested, although the Pearson Chi-Square did not reach conventional significance, requiring further research for confirmation.
Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method Hadianti, Sri; Aziz, Faruq; Nur Sulistyowati, Daning; Riana, Dwiza; Saputra, Ridwan; Kurniawantoro
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.37

Abstract

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.

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