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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Baja Health Science
Budi Utomo
Universitas Indonesia
Computer Science & IT Engineering Health Professions Medicine & Pharmacology Public Health

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Effectiveness of Fogging With Spatial Analysis in The Working Area of The East Bogor Health Center Ratna Dwi Lestari; Budi Utomo
JOURNAL OF BAJA HEALTH SCIENCE Vol 3 No 01 (2023): Journal of Baja Health Science
Publisher : Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/joubahs.v3i01.2452

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes aegypti mosquito. DHF is a frightening disease because its transmission can take place quickly in an area. Even in one month, the number of dengue cases in endemic areas can reach tens of people infected with dengue virus. Maximizing the DHF control program at the local health office and puskesmas is the main key in tackling the spread of DHF. However, it is a current obstacle that has made the DHF control program ineffective in Bogor City, namely the absence of scientific predictions about the location of DHF vulnerable areas in Bogor City, including in the working area of the East Bogor Health Center. So that DHF control programs such as fogging have not been able to significantly reduce DHF cases. This study used observational analysis with a cross-sectional design. The data collected was then analyzed using spatial analysis with the buffer method. The results showed that DHF cases were almost spread throughout the working area of the East Bogor Health Center. Based on interviews with puskesmas officers, the fogging radius is around 200 meters from the fogging location. After carrying out a spatial analysis using the buffer method, it was found that the fogging radius only reached a small part of the East Bogor Health Center work area which was indicated to be a DHF-prone area. Implementation of fogging programs that are not based on DHF vulnerable areas results in ineffective prevention of DHF. Therefore, spatial-based DHF mapping is needed to identify areas that are vulnerable to DHF so that it can be used as a reference in determining fogging locations.
Hypertension prediction using machine learning algorithm among Indonesian adults Rico Kurniawan; Budi Utomo; Kemal N. Siregar; Kalamullah Ramli; Besral Besral; Ruddy J. Suhatril; Okky Assetya Pratiwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp776-784

Abstract

Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.
Co-Authors Besral . Kalamullah Ramli Kemal N. Siregar Okky Assetya Pratiwi Ratna Dwi Lestari Ruddy J Suhatril