A Prediction Modelling for Dengue Fever Outbreaks Using Data Mining Techniques

Main Article Content

Jiraroj Tosasukul


Dengue fever has become one of Thailand's most critical concerns in recent years. The number of dengue cases from January to May 2021 in the area under the responsibility of the Office of Disease Prevention and Control 2 in Phitsanulok was the second highest in Thailand. Therefore, the objective of this research was to construct a reasonable model for predicting dengue fever outbreaks in Phitsanulok using data mining techniques. The data used were the dengue fever patient information from the Office of Disease Prevention and Control 2 in Phitsanulok, and the monthly weather data from the Northern Meteorological Center of 5 provinces. Four parameters were used to predict or categorize the symptoms of Dengue fever including personal data, time, location, and climate from January 1, 2009, to May 31, 2021, with a total of 47,386 items and 12 attributes. The data was evaluated by using four different data mining techniques which were Decision trees, Naïve Bayes, Artificial neural network (ANN), and Support vector machines techniques (SVM). Four attributes of the district, province, year, and monthly average precipitation were chosen to construct the models by using the selection technique. The results revealed that the most suitable model for forecasting dengue fever outbreaks was obtained from the Decision trees technique. It gave the highest accuracy and the f-measure of 69.83% and 75.4%, respectively. The forecasting model developed in this study could be applied for policy planning and campaigning to prevent the dengue epidemic in the high-risk population under limited budgets and resources.

Article Details

How to Cite
Tosasukul, J. (2021). A Prediction Modelling for Dengue Fever Outbreaks Using Data Mining Techniques. PKRU SciTech Journal, 5(2), 51–60. Retrieved from https://ph01.tci-thaijo.org/index.php/pkruscitech/article/view/245824
Research Articles


Wang, W. H., Urbina, A. N., Chang, M. R., Assavalapsakul, W., Lu, P. L., Chen, Y. H., & Wang, S. F. (2020). Dengue hemorrhagic fever–a systemic literature review of current perspectives on pathogenesis, prevention and control. Journal of Microbiology Immunology and Infection, 53, 963-978.

Liu, K., Hou, X., Ren, Z., Lowe, R., Wang, Y., Li, R., Liu, X., Sun, J., Lu, L., Song, X., & Wu, H. (2020). Climate factors and the east Asian summer monsoon may drive large outbreaks of dengue in China. Environmental Research, 183, 109-190.

สุรเกียรติ อาชานานุภาพ. (2553). ตำราการตรวจรักษาโรคทั่วไป เล่ม 2 : 350 โรค กับการดูแลรักษาและการป้องกัน (พิมพ์ครั้งที่ 5). กรุงเทพฯ: โฮลิสติก พับลิชชิ่ง.

กรมควบคุมโรค. (2563). รายงานพยากรณ์โรค 2563. [ออนไลน์], สืบค้นจาก https://ddc.moph.go.th/dvb/forecast_detail.php?publish=10268 (28 มีนาคม 2564).

ศรัณรัชต์ ชาญประโคน, ดารินทร์ อารีย์โชคชัย, ปณิธี ธัมมวิจยะ, และจิระพัฒน์ เกตุแก้ว. (2561). รายงานพยากรณ์โรคไข้เลือดออก พ.ศ. 2561. รายงานการวิจัย. กรุงเทพฯ: สำนักโรคติดต่อนำโดยแมลง สำนักระบาดวิทยา กรมควบคุมโรค.

รณกร สมสกุล. (2564). รายงานเฝ้าระวังทางระบาดวิทยา เขตสุขภาพที่ 2. รายงานการวิจัย. พิษณุโลก: สำนักงานป้องกันควบคุมโรคที่ 2 จังหวัดพิษณุโลก.

Muhilthini, P., Meenakshi, B. S., Lekha, S. L., & Santhanalakshmi, S. T. (2018). Dengue possibility forecasting model using machine learning algorithms. International Research Journal of Engineering and Technology, 5, 1661-1665.

Swaraj, P. K., & Kiruthiga, G. (2021). Design and analysis on medical image classification for dengue detection using artificial neural network classifier. ICTACT Journal on Image and Video Processing (IJIVP), 11, 2407-2412.

Salim, N. A., Wah, Y. B., Reeves, C., Smith, M., Yaacob, W. F. W., Mudin, R. N., Dapari, R., Sapri, N. N. F. F., & Haque, U. (2021). Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Scientific Reports, 11, 1-9.

Somwanshi, H., & Ganjewar, P. (2018). Real-time dengue prediction using naive Bayes predicator in the IoT (pp 725 - 728). In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). India.

Sarma, D., Hossain, S., Mittra, T., Bhuiya, M. A., Saha, I., & Chakma, R. (2020). Dengue prediction using machine learning algorithms (pp 1 - 6). In 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC). Malaysia.

Kamarudin, A. N., Zainol, Z., & Kassim, N. F. (2021). Forecasting the dengue outbreak using machine learning algorithm: a review (pp 1 - 5). In 2021 International Conference of Women in Data Science at Taif University (WiDSTaif). Saudi Arabia.

Freeze, J., Erraguntla, M., & Verma, A. (2018). Data integration and predictive analysis system for disease prophylaxis: Incorporating dengue fever forecasts (pp 913 - 922). In Proceedings of the 51st Hawaii International Conference on System Sciences. United States.