Comparison of Recent Data Mining Algorithms to Identify of the factors and effects of Dengue Fever and Ensemble Random Forest, A new Algorithm
International Research Journal on Advanced Science Hub,
2022, Volume 4, Issue 05, Pages 143-153
The disease dengue has created panic in the minds of men and women of this time. Now a day the menacing of dengue has spread from town areas to rural areas. It affects heavily works on body organs and leads to the final state of death. It works for some years on human organs even after coming round from it. It exists in the human body.This disease is not confined now in the congested town area only, but it has broken out in full swing in the rural area. We aim is to identify the factors which are the causes of the origin of dengue and its spread over society at such a large scale. It is also our aim to find the areas of soci- ety; on which consistent endeavor will help to confine in or diminish its effect in the 0- level. Information is collected on at random survey basis, especially from peoples of dengue affected area by Questionnaire Method. Intelligence is also gathered from hospital and Internet to collect data which help to indi- cate factors performed heavily in which situation of Society. We reached the conclusion by experiment worked in the past- information and present data.
Arafiyah, Ria, and Fariani Hermin. “Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method”. Journal of Physics: Con- ference Series 948 (2018). 10.1088/1742- 6596/ 948/1/012077.
Balasaravanan, K. and M. Prakash. “Detection of dengue disease using artificial neural net- work based classification technique”. Interna- tional Journal of Engineering & Technology 7.1.3 (2017): 13–13. 10.14419/ijet.v7i1.3.8978.
Caicedo-Torres, A´ ngelpaternina William, 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.
Chang, Ko. “Dengue fever scoring system: new strategy for the early detection of acute dengue virus infection in Taiwan”. Journal of the Formosan Medical Association 108 (2009): 879–885. 10.1016/S0929-6646(09)60420-4.
Chien, et al. “An app detecting dengue fever in children: using sequencing symptom patterns for a web-based assessment”. JMIR mHealth and uHealth 7.5 (2019). 10.2196/11461.
Fathima, A and D Manimegalai. “Predictive analysis for the arbovirus-dengue using svm classifica- tion”. International Journal of Engineering and Technology 2.3 (2012): 521–528. 10 . 1 . 1 . 411 .
Gomes, Ana and V Lisa. “Classification of dengue fever patients based on gene expression data using support vector machines”. PloS one 5 (2010). 10. 1371/journal.pone.0011267.
Hasan, Shamimul, et al. “Dengue virus: A global human threat: Review of literature”. Journal of International Society of Preventive and Commu- nity Dentistry 6.1 (2016): 1–1. 10 . 4103 / 2231 -0762.175416.
I Nordin, N. “The Classification Performance using Support Vector Machine for Endemic Dengue Cases”. Journal of Physics: Conference Series 1496.1 (2020). 10 . 1088 / 1742 - 6596 / 1496 % 20 /1/012006.
Kapoor, Rajeev, Virender Kadyan, and Sachin Ahuja. “Identification of Influential Parameter for Early Detection of Dengue Using Machine Learn- ing Approach”. Proceedings of the 5th Interna- tional Conference on Cyber Security & Privacy in Communication Networks (ICCS). 2019 (). 10. 2139/ssrn.3511419.
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.
Rohan, Tanbin and Islam. “A precise breast can- cer detection approach using ensemble of random forest with AdaBoost”. 2019 International Con- ference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (2019). 10 . 1109 / IC4ME % 20247184 . 2019 .9036697.
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.
Salami, Donald. “Predicting dengue importation into Europe, using machine learning and model- agnostic methods”. Scientific Reports 10 (2020). 10.1038/s41598-020-66650-1.
Silitonga and Permatasari. “Evaluation of Dengue Model Performances Developed Using Artificial Neural Network and Random Forest Classifiers”.Procedia Computer Science 179 (2021). 10.1016/ j.procs.2020.12.%20018.
- Article View: 93
- PDF Download: 63