Improved Ant Colony on Feature Selection and Weighted Ensemble to Neural Network Based Multimodal Disease Risk Prediction (WENN-MDRP) Classifier for Disease Prediction Over Big Data

Authors

  • Gakwaya Nkundimana Joel
  • S Manju Priya

DOI:

https://doi.org/10.14419/ijet.v7i3.27.17654

Published:

2018-08-15

Keywords:

Disease prediction, machine learning, ensemble, neural network, prediction accuracy.

Abstract

As the big data is growing in biomedical and healthcare communities, so are precise analyses of medical data aids, premature disease identification, patient care as well as community services. On the other hand, the accuracy of the analysis decreases, if the medical data quality is imperfect. As a result, the choice of features from the dataset turns out to be an extremely significant task. Feature selection has exposed its efficiency in numerous applications by means of constructing modest and more comprehensive models, enlightening learning performance and preparing clean and clear data. The proposed method analyzes the difficulties of feature selection for big data analytics.  Improved Ant Colony Optimization based Feature Selection (IACO) algorithm is presented for resolving this issue. The reconstruction of missing data before the incomplete data available was performed with help of latent factor mode. Therefore, it was not easy to choose the best features from the structured and unstructured data. the unheard technique which is called Weighted Ensemble Based Neural Network for multimodal disease risk prediction(WENN-MDRP) algorithm is implemented in order to provide the best features selection among structured as well as unstructured data. The research method provides improved prediction accuracy when matched with conventional techniques. In the MATLAB environment, the presented classifiers are implemented. The outcomes are computed in regard to recall, precision, accuracy, f-measure and error rate.

 

 

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