Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Real Time Dengue Prediction Using Machine Learning


Affiliations
1 Assistant Professor, Department of CSE, IFET College of Engineering, Villupuram, India
2 UG Scholar, Department of CSE, IFET College of Engineering, Villupuram, India
     

   Subscribe/Renew Journal


Context: Dengue is generally spreading the endemic zones for atmosphere zones. In a whole world, transmitted to an individual by an Aedes Aegyptus mosquito, dengue load in India is expanding at an upsetting rate. The commitments of expanded versatility, both vector and human populaces, urbanization and atmosphere changes are the most critical factors to clarify the expanding episode of dengue. Generally, the different calculations looked at, it was wasteful to evaluate the exactness for early dengue illness expectation. The recommended framework is to build up an application for Smart Prognosis Dengue (SPD) Model for AI development to foresee constant Dengue illness. It will continue with unmistakable AI approaches going from basic classifiers like Decision Tree, Logistic Regression. Thus, the Logistic Regression Algorithm gives the most extreme exactness precision will analyse for the dengue expectation. By utilizing both the equipment and programming setup, it joins the AI ideas with the expectation calculation and furthermore gives the framework can be altered to produce risk alert and area explicit forecasts.

Keywords

Machine Learning, Logistics Regression Algorithm, Raspberry Pi 3, GSM Module, GPS Module.
Subscription Login to verify subscription
User
Notifications
Font Size


Abstract Views: 53

PDF Views: 0




  • Real Time Dengue Prediction Using Machine Learning

Abstract Views: 53  |  PDF Views: 0

Authors

A. Divya
Assistant Professor, Department of CSE, IFET College of Engineering, Villupuram, India
S. Lavanya
UG Scholar, Department of CSE, IFET College of Engineering, Villupuram, India

Abstract


Context: Dengue is generally spreading the endemic zones for atmosphere zones. In a whole world, transmitted to an individual by an Aedes Aegyptus mosquito, dengue load in India is expanding at an upsetting rate. The commitments of expanded versatility, both vector and human populaces, urbanization and atmosphere changes are the most critical factors to clarify the expanding episode of dengue. Generally, the different calculations looked at, it was wasteful to evaluate the exactness for early dengue illness expectation. The recommended framework is to build up an application for Smart Prognosis Dengue (SPD) Model for AI development to foresee constant Dengue illness. It will continue with unmistakable AI approaches going from basic classifiers like Decision Tree, Logistic Regression. Thus, the Logistic Regression Algorithm gives the most extreme exactness precision will analyse for the dengue expectation. By utilizing both the equipment and programming setup, it joins the AI ideas with the expectation calculation and furthermore gives the framework can be altered to produce risk alert and area explicit forecasts.

Keywords


Machine Learning, Logistics Regression Algorithm, Raspberry Pi 3, GSM Module, GPS Module.



DOI: https://doi.org/10.37506/v11%2Fi2%2F2020%2Fijphrd%2F194834