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Dwiza Riana
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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 5 Documents
 1 
Search results for , issue "Volume 1 No. 1, March 2023" : 5 Documents clear
Segmentation in Identifying the Development of Ground Glass Opacity on CT-Scan Images of the Lungs Na`am, Jufriadif
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.1

Abstract

Ground Glass Opacity (GGO) in the image of the lungs is an object that is white in color. The image was recorded using a Computerized Tomography Scan (CT-Scan). This object has very similar color features to other objects in the lung image, making it very difficult to identify precisely. Likewise by observing the development of this object every time from recording continuously. This study aims to segment the GGO on CT-Scan images that are examined repeatedly due to an increase in complaints against patients. The processed image is an image of the lungs from the CT-Scan equipment. Patients were recorded twice at different time intervals. The processed image is an axial slice of the data cavity as a whole, totaling 12 images for each patient in each recording. The tool used for recording is a CT-Scan with the General Electric (GE) brand model D3162T. The method used is parallel processing with a combination of Image Enhancement techniques, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify the development of the GGO pixel size well, where the increasing number of patient complaints, the larger the GGO area. The extent of development of GGO is irregular with respect to time and examination. Each patient experienced an expansion of GGO by an average of 0.54% to 1.89%. This study is very good and can correctly identify ARF, so it can be used to measure the level of development of ARF in patients with accuracy.
Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool Octaviantara, Adi; Abbas, Moch Anwar; Azhari, Ahmad; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.2

Abstract

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.
Logistic Regression with Hyper Parameter Tuning Optimization for Heart Failure Prediction Herwanto, Teguh; Kodri, Wan Ahmad Gazali; Aziz, Faruq; Hewiz, Alya Shafira; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.3

Abstract

Heart failure is a major public health concern that causes a substantial number of deaths worldwide. Risk factor analysis is required to diagnose and treat patients with heart failure. The logistic regression with hyper parameter tuning optimization is presented in this research, with ejection fraction, high blood pressure, age, and  serum creatinine as relevant risk factors. This study indicates that better data preparation utilizing Deep Learning with hyper parameter adjustment be used to determine the best parameter that has a substantial influence as a risk factor for heart failure. The experiments employed data from the Faisalabad Institute of Cardiology and Allied  Hospital in Faisalabad (Punjab, Pakistan), which included 299 samples. The experimental findings reveal that the proposed approach obtains a recall of 63.16% greater than related works.
Optimizing Lung Cancer Prediction Using Evaluating Classification Methods and Sampling Techniques Metalica, Dika Putri; Marasabessy, Fahmi B
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.4

Abstract

Lung cancer is an extremely aggressive type of cancer and one of the leading causes of death globally. The focus of this study is to improve the detection and prediction of lung cancer by evaluating different approaches for classification and sampling. The research utilizes a dataset comprising 1000 patients and 24 Attributes. The primary goal is to compare the effectiveness of classification methods like Logistic Regression, AdaBoost, and GradientBoosting, in conjunction with diverse sampling techniques such as Random Over-Sampling, RandomUnder-Sampling, and SMOTE by Level Considering, for predicting lung cancer. The assessment metrics includeaccuracy, precision, recall, and F1-score. The experimental findings demonstrate that Gradient Boosting (GBoost) attains flawless accuracy, precision, recall, and F1-score results of 100% when identifying lung cancer instances within the dataset. This highlights the effectiveness of GBoost in accurately predicting lung cancer occurrence. The findings of this research aim to contribute significantly to the development of more effective diagnostic and predictive methods for lung cancer. 
Optimization of Breast Cancer Prediction using Optimaze Parameter on Machine Learning Nuarini, Sri; Rumintarsih, Ade
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.5

Abstract

At present, a very common cancer disease in women is breast cancer. This cancer develops in the female breast tissue and is the cancer with the highest mortality rate. This needs great attention. Forecasting breast cancer has been studied by a number of researchers and is considered a serious threat to women. Clinical difficulties in creating treatment approaches that will help patients live longer, due to the lack of solid predictive models that can predict outcomes at an early stage by analyzing patient history data. Because it can affect women all over the world. Early detection of breast cancer is crucial in determining the path of action. Cancer types can be distinguished into two types: benign and malignant. this research aims to provide information and science to medical professionals and also cancer patients to know the classification of the two types of cancer. The research project aims to also leverage data mining techniques using several algorithms on Machine Learning (ML) such as Decision Tree(DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting Tress (XGBoost). The results of this algorithm will determine the prediction of the most common types of cancer. The study used 683 samples of breast cancer patients, including 10 characteristics. This test is measured through mammography and biopsy tests. Using K-Fold Validation operators, then the sresults of the study showed that the K-Nearest Neighbor (KNN) algorithm produced the highest accuracy of 96.87% compared to the other five algorithms. Then, as a comparison again, the researchers also optimized the accuracy value using the parameter optimize operator. Where the number produced becomes more overwhelming. The highest accuracy result after calculated with the parameter optimize is the Random Forest (RF) algorithm. Where the result is 100% accurate compared to other ML algorithms. 

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