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Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan
Published by Universitas Jambi
ISSN : 2339269X     EISSN : 25806874     DOI : -
Core Subject : Health,
Medicine & Pharmacology
Jambi Medical Journal is a Journal for Medical And Health Issues, in Scope: Medical Education, Farmakology, Mikrobiology, PUblic Health, Clinical Patology, Medical Nutrition, Clinical Medicine, Pediatric, Immunology, Patology Anatomi, Orthopedy, Obstetri and Gynekology, Internal Medicine, Endocrine and Metabolic, Genetics & Molecular Biology.
Arjuna Subject : -
Articles 393 Documents
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The Effect of CLAHE Enhancement for Breast Cancer RSNA Detection Putra, Gregorius Guntur Sunardi; Mafazy, Muhammad Meftah
Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan Vol. 14 No. 1 (2026): JAMBI MEDICAL JOURNAL: Jurnal Kedokteran dan Kesehatan
Publisher : FAKULTAS KEDOKTERAN DAN ILMU KESEHATAN UNIVERSITAS JAMBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jmj.v14i1.52596

Abstract

Background: In recent years, image detection has gained substantial importance within the medical field, particularly in diagnosing and interpreting diseases such as breast cancer. Breast cancer stands as a formidable threat, constituting approximately 30% of newly diagnosed cancer cases in the United States and contributing to 12.5% of all new cancer cases. Early detection is crucial for preventing the progression of this severe ailment. Method: This research endeavours to leverage deep learning methodologies to ascertain the presence or absence of breast cancer in patients, utilizing the mammograph breast cancer RSNA dataset. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances the dataset's images for more precise results. The focus of this study is specifically on the biopsy status related to breast cancer. The deep learning algorithms implemented encompass ResNet50, VGG16, and EfficientNetB0. Notably, the dataset is confined to biopsy status, streamlining the investigation to this critical aspect. Result: The experimental results reveal that the ResNet50 model achieved the highest accuracy at 61%, coupled with an F1-Score of 0.47%. These findings underscore the potential of deep learning techniques, particularly ResNet50, in aiding the early detection of breast cancer. Conclusion: Incorporating image enhancement techniques like CLAHE adds an extra layer of refinement to the dataset, contributing to the overall accuracy and reliability of the diagnostic process. As medical image analysis continues to evolve, such studies pave the way for advancements in early disease detection and intervention strategies.
Development Of Machine Learning Based On Vascular Risk Factors To Assess Stroke Recurrence Using The ESRS (The Essen Stroke Risk Score) Purnama, Sari Dianita; Setianto, Catur Ari
Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan Vol. 14 No. 1 (2026): JAMBI MEDICAL JOURNAL: Jurnal Kedokteran dan Kesehatan
Publisher : FAKULTAS KEDOKTERAN DAN ILMU KESEHATAN UNIVERSITAS JAMBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jmj.v14i1.52609

Abstract

Background: Recurrent stroke is a major cause of increased morbidity and disability. Traditional scores, the ESRS indicates that this score has limited discriminatory ability. The study aims to develop a Machine Learning (ML) model based on individual ESRS components that can surpass the traditional score’s accuracy for stroke recurrence risk. Methods: The study employs a Comparative Cross-Sectional design, with a total of 115 data classified into First-Time Stroke and Recurrent Stroke. Preprocessing with SMOTE for class imbalance. The classification model was built using the Random Forest algorithm and validated with 10-Fold Stratified Cross-Validation in WEKA software. Results: The Optimal ML Model achieved a superior Area Under the Curve (AUC) of 0.949 (significantly exceeding the traditional ESRS AUC of 0.55–0.58), demonstrating extremely strong discriminatory capability. Conversely, the traditional ESRS Model showed very low Accuracy and poor Precision. Conclusion: The development of the Random Forest Machine Learning model based on individual vascular risk factor components proved to be significantly superior in assessing stroke recurrence risk compared to traditional ESRS risk stratification. With an AUC of 0.949. These findings justify the potential integration of the ML model into Clinical Decision Support Systems (CDSS).
Digital Exposure and Physical Performance : The Relationship Between Screen Time and Motor Strength in Adolescents Ardillah, Dinda Rizky; Tjhin, Purnamawati
Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan Vol. 14 No. 1 (2026): JAMBI MEDICAL JOURNAL: Jurnal Kedokteran dan Kesehatan
Publisher : FAKULTAS KEDOKTERAN DAN ILMU KESEHATAN UNIVERSITAS JAMBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jmj.v14i1.52818

Abstract

Background: Rapid advancements in digital technology have led to increased gadget use among adolescents, resulting in prolonged screen time among senior high school students. Although screen time supports learning and access to infomation, excessive use may reduce physical activity, impaired sleep quality, and limit social interaction, potentially affecting motor strength. Methods: This cross-sectional analytic observational study involved 228 senior high school students aged 15-18 years in Tangerang Regency, selected using stratified random sampling. Screen time was assessed using the Questionnaire for Screen Time of Adolescents (QueST), while motor strength was measured with a Camry handgrip dynamometer. Data were analyzed using the Chi-square test with SPSS. Results: Most respondents reported high screen time (73.24%) and low motor strength (58.77%). Students with high screen time were more likely to have low motor strength (70.66%), whereas those with low screen time more frequently demonstrated strong motor strength (59.26%). Chi-square analysis revealed a significant associations between screen time and motor strength (p<0.001). Conclusion: High screen time is significantly associated with reduced motor strength among senior high schools students. This highlight the importance of balanced screen use to support adolescents' physical development and promoting healthier daily activity patterns.

Page 40 of 40 | Total Record : 393

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