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ESVM-SWRF: Ensemble SVM-based sample weighted random forests for liver disease classification.

S Padmakala1, C A Subasini1, S P Karuppiah2

  • 1Department of CSE, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India.

International Journal for Numerical Methods in Biomedical Engineering
|August 25, 2021
PubMed
Summary

This study introduces an ensemble SVM-based sample weighted random forests (eSVM-swRF) model optimized with a novel improved colliding body optimization (NICBO) algorithm for accurate liver disease prediction. The proposed method significantly outperforms existing approaches in diagnosing liver conditions.

Keywords:
ensemble SVMliver disease predictionnovel improved colliding boding optimizationsample weighted random forest

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Area of Science:

  • Medical data mining
  • Machine learning in healthcare
  • Disease prediction and diagnosis

Background:

  • Healthcare faces challenges with large datasets for disease analysis and prediction.
  • Existing data mining models have limitations including high execution time, computational complexity, and slow convergence.
  • Effective data transformation into valuable insights is crucial for accurate medical decision-making.

Purpose of the Study:

  • To propose an advanced ensemble model for predicting liver diseases.
  • To address the shortcomings of existing methods in terms of efficiency and accuracy.
  • To optimize model parameters using a novel metaheuristic algorithm.

Main Methods:

  • Proposed an ensemble SVM-based sample weighted random forests (eSVM-swRF) model.
  • Utilized extraction, loading, transformation, and analysis (ELTA) for data pre-processing.
  • Employed a novel improved colliding body optimization (NICBO) algorithm to optimize eSVM-swRF parameters (P, T, mTry).
  • Validated the model using the UCI liver disease dataset and RapidMiner Studio.

Main Results:

  • The eSVM-swRF model with NICBO optimization demonstrated outstanding performance.
  • Achieved superior prediction accuracy compared to existing methods like PSO-SVM, FuzzyANWKNN, NB-SVM, and Neural Network.
  • The proposed method effectively handles complex medical data for disease prediction.

Conclusions:

  • The developed eSVM-swRF with NICBO algorithm offers a highly effective solution for liver disease prediction.
  • This approach overcomes limitations of previous methods, providing a scalable and efficient diagnostic tool.
  • The study highlights the potential of advanced machine learning techniques in medical data mining for improved healthcare outcomes.