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Window of Health : Jurnal Kesehatan
Published by Universitas Muslim Indonesia
ISSN : -     EISSN : 26145375     DOI : -
Core Subject : Health,
Medicine & Pharmacology Nursing Public Health
Window of Health is a media publication of scientific works in the field of health in a broad sense such as public health, nursing, midwifery, medicine, pharmacy, health psychology, nutrition, health technology, health analysis, health information system, medical record, health law, etc.
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue "Vol 7 No 2 (April 2024)" : 11 Documents clear
Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors Nasir, Muhammad; Aldillah Wulandhari, Shobiechah; Tenrisau, Dhihram; Haris Ibrahim, Muhammad; Rahastri, Ajeng; Sa’adatar Rohmah, Nilna; Surya, Asik; Thohir, Burhanuddin; Aryani, Desfalina; Firdaus Kasim, Muhammad
Window of Health : Jurnal Kesehatan Vol 7 No 2 (April 2024)
Publisher : Fakultas Kesehatan Masyarakat Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/woh.vi.1428

Abstract

Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, shiny.io). The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.

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