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Mortality Prediction of Victims in Road Traffic Accidents (RTAs) in India using Opposite Population SGO-DE based Prediction Model

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Title Mortality Prediction of Victims in Road Traffic Accidents (RTAs) in India using Opposite Population SGO-DE based Prediction Model
 
Creator Jena, Junali Jasmine
Satapathy, Suresh Chandra
 
Subject Opp-SGO-DE
Parameter Tuning
Random Forest
Support Vector Machine
 
Description 1001-1007
Getting immediate and appropriate care for the victims of Road Traffic Accidents (RTAs) in countries like India with huge population is a challenging job. In this paper a new hybridized evolutionary algorithm has been proposed for hyper-parameter tuning of the hyper-parameters of the prediction models using which mortality prediction of victims of RTAs in India have been performed. The proposed methodology Opp-SGO-DE has been used for parameter tuning in prediction algorithms like Random Forest (RF) and Support Vector Machine (SVM) and promising results were found from the experimentation. In RF, accuracy was increased from 0.75 to 0.82 and F1-score was increased from 0.66 to 0.77 in dataset-1 and accuracy was increased from 0.66 to 0.75 and F1-score was increased from 0.62 to 0.65 in dataset-2. In SVM, accuracy was increased from 0.63 to 0.74 and F1-score was increased from 0.58 to 0.67 in dataset-1 and accuracy was increased from 0.56 to 0.62 and F1-score was increased from 0.54 to 0.575 in dataset-2.
 
Date 2021-11-17T05:24:49Z
2021-11-17T05:24:49Z
2021-11
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58522
 
Language en
 
Publisher CSIR-NIScPR
 
Source JSIR Vol.80(11) [November 2021]