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Green Health: Journal of Health Sciences, Nursing and Nutrition

GreenHealth
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Published by International Forum of Researchers and Lecturers

ISSN : - EISSN : 30637309 DOI : 10.70062

Core Subject : Health,

Nursing

Green Health: International Journal of Health Sciences, Nursing and Nutrition; This journal is intended for the publication of scientific articles published by the International Forum of Researchers and Lecturers. This journal contains studies in the fields of Health Sciences, Nursing and Nutrition, both theoretically and empirically. The focus of this journal is on the study of Science, Nursing Science, Midwifery, Hospital Administration, Entomology (Health, Phytopathology), Biomedical Science, Medical Analysis, Reproduction (Biology and Health), Nutrition Science, and Other Health & Nutrition Not Yet Listed. This journal is published 1 year 4 times (January, April, July and October).

Arjuna Subject : Keperawatan - Nutrition and Dietetics

Articles 52 Documents

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Integrated Maternal Health Model for Early Preeclampsia Prevention: A Community Health Center-Based Study in Medan, Indonesia Aida Fitria; Devi Nallappan; Fina Kusuma Wardani; Dian Zuiatna; M.Crystandy
Green Health International Journal of Health Sciences Nursing and Nutrition Vol. 3 No. 2 (2026): April: Green Health: Journal of Health Sciences, Nursing and Nutrition
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenhealth.v3i2.311

Preeclampsia remains a major contributor to maternal morbidity and mortality, particularly in developing countries such as Indonesia. Early detection and integrated management at the primary healthcare level are essential to prevent disease progression and improve maternal outcomes. Modifiable risk factors such as maternal obesity, inadequate calcium intake, and poor clinical management contribute significantly to disease progression (WHO, 2021; Zhang et al., 2020). This study aimed to evaluate the effectiveness of an Integrated Maternal Health Model (IMHM) combining risk assessment, nutritional intervention, and clinical management for early prevention and control of preeclampsia in primary healthcare settings. A quasi-experimental cohort study was conducted among 104 pregnant women, consisting of 52 preeclamptic and 52 normotensive participants in community health centers in Medan, Indonesia. Data were collected across four antenatal visits, including blood pressure measurements, proteinuria (dipstick), calcium intake, supplementation adherence, and antihypertensive therapy. Statistical analyses included bivariate and longitudinal tests. The results showed that maternal obesity, history of preeclampsia, and hypertension were significant risk factors. Adequate calcium intake demonstrated a protective effect against preeclampsia (p < 0.05), consistent with recent evidence indicating that calcium supplementation can reduce the risk of preeclampsia by up to 49%. However, calcium supplementation did not significantly influence blood pressure among normotensive pregnant women. Antihypertensive therapy, particularly intensive nifedipine regimens, showed significant differences in blood pressure patterns across visits (p < 0.05). In addition, proteinuria levels significantly decreased over time (p < 0.001), indicating improvement in renal function. In conclusion, the IMHM is effective in improving maternal outcomes through a multi-component approach integrating clinical, nutritional, and monitoring strategies. This model provides a practical and scalable framework for early prevention and management of preeclampsia in primary healthcare settings.

Implementation of Graphical User Interface (GUI) for Thyroid Gland Ultrasonography Image Segmentation and Visualization Application Based on Deep Learning Ana Septiana; Edy Susanto; Agung Nugroho Setiawan; Dicky Choirriyan
Green Health International Journal of Health Sciences Nursing and Nutrition Vol. 3 No. 2 (2026): April: Green Health: Journal of Health Sciences, Nursing and Nutrition
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenhealth.v3i2.312

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

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G Thippanna

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Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

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Jawa tengah

INDONESIA

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name G Thippanna

Contact Email indexsasi@apji.org

Phone +6282359594933

Journal Mail Official info@ifrel.org

Editorial Address Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

Location Kab. demak, Jawa tengah INDONESIA

Abstract

Abstract

Contact Name
G Thippanna

Contact Email
indexsasi@apji.org

Phone
+6282359594933

Journal Mail Official
info@ifrel.org

Editorial Address
Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

Location
Kab. demak,

Jawa tengah

INDONESIA