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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. 3, September 2023" : 5 Documents clear
A Tripartite Machine Learning Approach for Accurate Prognosis of COVID-19 Patient Survival Faruq Aziz
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.13

Abstract

Accurate prognosis of COVID-19 patient survival is vital for healthcare decision-making. This research proposes a tripartite machine learning approach that integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost for outcome prediction. Our hybrid model exploits the strengths of individual algorithms and combines their predictions using a weighted ensemble. Leveraging clinical data, KNN captures local patterns, SVM finds complex boundaries, and XGBoost enhances overall performance. Experimental results show exceptional precision (0.93), recall (0.93), and F1-score (0.93) for both classes, affirming accurate classification of "Alive" and "Died" cases. The achieved accuracy of 0.93 further demonstrates the reliability of the proposed approach. Our tripartite method holds the potential to enhance COVID-19 survival prediction, providing valuable insights for clinical practitioners and policymakers. This study contributes by seamlessly fusing KNN, SVM, and XGBoost models into a robust predictive tool, thereby aiding medical professionals in informed decision-making for patient care and resource allocation. The demonstrated success underscores the efficacy of a combined approach, highlighting its relevance in accurately predicting patient outcomes.
Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model Daniati Uki Eka Saputri; Nurul Khasanah; Faruq Aziz; Taopik Hidayat
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.14

Abstract

Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.
Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction Sri Hadianti; Wan Ahmad Gazali Kodri
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.15

Abstract

Heart disease prediction is a critical area in healthcare, as early identification and accurate assessment of cardiovascular risks can lead to improved patient outcomes. This study explores the application of machine learning techniques for predicting heart disease. Various data attributes, including medical history, clinical measurements, and lifestyle factors, are utilized to develop predictive models. A comprehensive analysis of different machine learning algorithms is conducted to determine their efficacy in classification tasks. The dataset used for experimentation is sourced from a diverse patient population, enhancing the generalizability of the findings. Through rigorous evaluation and validation, the study aims to identify the most suitable machine learning approach for effectively predicting heart disease. The results highlight the potential of machine learning as a valuable tool in assisting healthcare professionals in making informed decisions and providing personalized care to individuals at risk of heart disease
The Relationship between Age, Education, and Maternal Employment with Exclusive Breastfeeding in Children Aged 6 - 23 Months in Kalirejo, Malang Regency Nanda Amalia Ramadhanti; Muhammad Rifqo Hafidzudin Farid; Salma Fadila; Adristi Hanun Naziliah; Putu Laksmi Febriyani; Clarisa Christina Gabriella; Alya Shafira Hewiz
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.17

Abstract

The target percentage of infants under 6 months old receiving exclusive breastfeeding is 40%. However, in 2020, in Kalirejo Sub-district, the number was 9.04%. This was presumably caused by the high number of working mothers. Therefore, this study was conducted to analyze the relationship between age, education, and occupation of mothers regarding exclusive breastfeeding in children aged 6-23 months in Kalirejo Sub-district, Malang Regency. The study design employed in this research was observational analytics with a cross-sectional design. The research population consisted of mothers residing in Kalirejo Sub-district, Malang Regency. The required sample size was 66 individuals. A questionnaire was used to collect research data. The required data was processed using SPSS software with the Chi-Square Test analysis technique. The result showed that 78.8% mothers were in the age group of 20-35%, and 21.2% were in the age group of >35 years. Based on the highest education level attained by the respondents, 1.5% had completed elementary school, 15.2% had completed junior high school, 57.6% had completed high school, and 25.8% had completed tertiary education. About 51.5% of respondents were employed, while 48.5% were not employed. The number of respondents' children receiving exclusive breastfeeding was 50%. The analysis indicated a relationship between occupation and exclusive breastfeeding with a p-value of 0.049 and a strength of relationship between the two variables at 0.236.
Comparison Algorithm on Machine Learning for Student Mental Health Data Sri Nuarini; Siti Fauziah; Nissa Almira Mayangky; Ridan Nurfalah
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.18

Abstract

The COVID-19 pandemic has posed unparalleled difficulties, encompassing substantial repercussions on the emotional well-being of students. This study utilises machine learning methodologies to forecast the mental health condition of students during and following the pandemic. The dataset consists of 11 distinct attributes and a total of 101 data points, which have been gathered from multiple sources. The preprocessing stage encompasses the removal of unnecessary characteristics, handling missing data, and partitioning the dataset into separate subsets for training and validation purposes. This study utilises three machine learning algorithms, namely RF, KNN, and NB, in order to make predictions regarding the potential need for psychiatric support among students. These algorithms are carefully optimised to enhance their predictive capabilities. Evaluation metrics commonly used in several fields of study. The findings suggest that the KNN and RF algorithms had outstanding performance, but the Naïve Bayes algorithm exhibited satisfactory accuracy and a balanced trade-off between precision and recall. The optimised models have practical consequences that may be applied at educational institutions and inform policymakers. These implications include the ability to provide tailored interventions and support services specifically designed for students who are facing mental health difficulties as a result of the epidemic. Future research endeavours encompass the need for additional improvement of existing models and the fostering of interdisciplinary collaboration. This study provides significant contributions to the field by examining the utilisation of machine learning techniques in addressing the mental health needs of students both during and after the epidemic.

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