Prediction of DHF disease spreading patterns using inverse distances weighted (IDW), ordinary and universal kriging

Prasetiyowati, Sri Suryani; Sibaroni, Yuliant

doi:10.1088/1742-6596/971/1/012010

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…Thus, feature expansion greatly affects the performance of the random forest model and can increase accuracy. In addition, the map produced by this research is better than research [4], [13], and [15]. This study combines random forest and ordinary kriging methods to produce a prediction map for the distribution of dengue incidence rates for the next three years.…”

Section: Discussionmentioning

confidence: 99%

“…The incidence rate of DHF at the point Χ₀ can be predicted using the data values of n neighboring samples Χᵢ and combining them linearly with λᵢ weighting [15].…”

Section: Ordinary Krigingmentioning

confidence: 99%

“…While research on kriging has been carried out by [15], [16], and [17]. This study [15] mosquito transmission process with humans.…”

Section: Introductionmentioning

confidence: 99%

“…While research on kriging has been carried out by [15], [16], and [17]. This study [15] mosquito transmission process with humans. These bacteria were distributed in three areas and observed for three years in a frequency of 4 seasons.…”

Section: Introductionmentioning

confidence: 99%

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DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

Ashok

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.

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Section: Discussionmentioning

confidence: 99%

“…The incidence rate of DHF at the point Χ₀ can be predicted using the data values of n neighboring samples Χᵢ and combining them linearly with λᵢ weighting [15].…”

Section: Ordinary Krigingmentioning

confidence: 99%

“…While research on kriging has been carried out by [15], [16], and [17]. This study [15] mosquito transmission process with humans.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

Ashok

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Besides predicting malaria, machine learning also used to predict the other disease such as DFH. In this research, predicting DFH disease spreading pattern were using inverse distance weight (IDW), ordinary and and universal kriging methods [13]. But this study just predicts the number of patients per year and predicts the DFH disease spreading pattern.…”

Section: Introductionmentioning

confidence: 99%

Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes

Irmanita

2021

RESTI

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Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.

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Subway travel risk evaluation during flood events based on smart card data

Sun

Wang

Lall

et al. 2022

Geomatics, Natural Hazards and Risk

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Prediction of DHF disease spreading patterns using inverse distances weighted (IDW), ordinary and universal kriging

Cited by 6 publications

References 5 publications

DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes

Subway travel risk evaluation during flood events based on smart card data

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