A Data Mining based study on Dengue Fever: A Review
International Research Journal on Advanced Science Hub,
2022, Volume 4, Issue 04, Pages 101-107
Dengue fever (DF) is amosquitoborne disease spread by female Aedes mosquito. Dengue transmission depends on the changing of climatic parame- ters like temperature, humidity, rainfall, as well as the congestion in an area, i.e., where the population density is high. In this review, we have highlighted the reasons of the occurrence of DF and methods for early detection of the same. Symptoms are the key points to diagnose the dengue patients. Many diseases like Malaria, Chikungunia, Typhoid, COVID-19, etc. have the com- mon symptoms of fever, body pain, eye pain, diarrhoea, etc. Few rare symp- toms have been identified for diagnosing DF using machine learning predictive model. Rare symptoms are skin disease, headache, abdominal pain for early detection of dengue.
Arafiyah, Ria and Fariani Hermin. “Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method”. Journal of Physics: Conference Series 948.1 (2018): 012077–012077. 10. 1088/1742-6596/948/1/012077.
Caicedo-Torres, William, A´ ngel Paternina, and Hernando Pinzo´n. “Machine learning models for early dengue severity prediction”. Ibero- American Conference on Artificial Intelligence (2016). 10.1007/978-3-319-47955-2 21.
Chadwick, David, et al. “Distinguishing dengue fever from other infections on the basis of sim- ple clinical and laboratory features: Application of logistic regression analysis”. Journal of Clini- cal Virology 35.2 (2006): 147–153. 10.1016/j.jcv.2005.06.002.
Chang, Ko. “Dengue fever scoring system: new strategy for the early detection of acute dengue virus infection in Taiwan”. Journal of the For- mosan Medical Association 108 (2009): 879–885. 10.1016/S0929-6646(09)60420-4.
Cheong, Yoon Ling, Pedro J. Leita˜o, and Tobia Lakes. “Assessment of land use factors associ- ated with dengue cases in Malaysia using Boosted Regression Trees”. Spatial and Spatio-temporal Epidemiology 10 (2014): 75–84. 10.1016/j.sste.2014.05.002.
Fathima, A and D Manimegalai. “Predictive analy- sis for the arbovirus-dengue using svm classifica- tion”. International Journal of Engineering and Technology 2.3 (2012): 521–528. 10 . 1 . 1 . 411 .9082.
I Nordin, N, et al. “The Classification Performance using Support Vector Machine for Endemic Dengue Cases”. Journal of Physics: Conference Series 1496.1 (2020): 012006–012006. 10.1088/ 1742-6596/1496/1/012006.
Mello-Roma´n, Jorge D., et al. “Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay”. Computational and Mathe- matical Methods in Medicine 2019 (2019): 1–7. 10.1155/2019/7307803.
Niriella, Madunil A., et al. “Identification of dengue patients with high risk of severe disease, using early clinical and laboratory features, in a resource-limited setting”. Archives of Virology 165.9 (2020): 2029–2035. 10.1007/s00705-020-04720-5.
Rosid, M A, et al. “Classification Of Dengue Hemorrhagic Disease Using Decision Tree With Id3 Algorithm”. Journal of Physics: Conference Series 1381.1 (2019): 012039–012039. 10.1088/ 1742-6596/1381/1/012039.
Sahak, Mohammad Nadir. “Dengue fever as an emerging disease in Afghanistan: Epidemiology of the first reported cases”. International Journal of Infectious Diseases 99 (2020): 23–27. 10.1016/ j.ijid.2020.07.033.
Salami, Donald. “Predicting dengue importation into Europe, using machine learning and model-agnostic methods”. Scientific Reports 10 (2020):1–13. 10.1038/s41598-020-66650-1.
Yue, Yujuan, et al. “Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014”. International Journal of Infectious Diseases 75 (2018): 39–48. 10.1016/j.ijid.2018.07.023.
- Article View: 142
- PDF Download: 77