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The Multiple Time Series Clinical Data Processing with Modified Artificial Bee Colony Algorithm and Artificial Neural Network


Affiliations
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, India
 

Objectives: The main objective of this research is to discover patient acuity or severity of illness for immediate practical use for clinicians by evaluating the use of multivariate time series modelling along with multiple models.

Methods/Statistical analysis: As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. In many situations, analyzing a time-series in isolation is reasonable. And also this scenario is used to increase the prediction accuracy and reducing the time complexity using optimization algorithm.

Findings: The various research works has been analyzed and evaluated. From the analysis, the multiple measurements support vector machine (MMSVM), multiple measurements random forest regression (MMRF) and improved particle swarm optimization (IPSO) algorithm, modified artificial bee colony algorithm (MABCA) to solve the multiple time series problems by maximizing the optimal feature information which found to be superior for higher performance in terms of accuracy, precision and recall. The proposed MABCA with transductive support vector machine (TSVM) and artificial neural network (ANN) is used to improve the classification performance.

Application/Improvements: The findings of this work prove that the graph search based method provides better result than other approaches.


Keywords

Data Mining, Multiple Measurements, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Modified Artificial Bee Colony Algorithm (MABCA) and Artificial Neural Network (ANN).
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  • The Multiple Time Series Clinical Data Processing with Modified Artificial Bee Colony Algorithm and Artificial Neural Network

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Authors

M. Priyanga
Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, India
K. Sasi Kala Rani
Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, India
M. Pavithra
Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, India
S. Yamunadevi
Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, India

Abstract


Objectives: The main objective of this research is to discover patient acuity or severity of illness for immediate practical use for clinicians by evaluating the use of multivariate time series modelling along with multiple models.

Methods/Statistical analysis: As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. In many situations, analyzing a time-series in isolation is reasonable. And also this scenario is used to increase the prediction accuracy and reducing the time complexity using optimization algorithm.

Findings: The various research works has been analyzed and evaluated. From the analysis, the multiple measurements support vector machine (MMSVM), multiple measurements random forest regression (MMRF) and improved particle swarm optimization (IPSO) algorithm, modified artificial bee colony algorithm (MABCA) to solve the multiple time series problems by maximizing the optimal feature information which found to be superior for higher performance in terms of accuracy, precision and recall. The proposed MABCA with transductive support vector machine (TSVM) and artificial neural network (ANN) is used to improve the classification performance.

Application/Improvements: The findings of this work prove that the graph search based method provides better result than other approaches.


Keywords


Data Mining, Multiple Measurements, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Modified Artificial Bee Colony Algorithm (MABCA) and Artificial Neural Network (ANN).

References