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HANN: A Hybrid Model for Liver Syndrome Classification by Feature Assortment Optimization.

L Anand1,2, S P Syed Ibrahim3

  • 1School of Computing Science and Engineering, VIT, Chennai, Tamil Nadu, India.

Journal of Medical Systems
|September 28, 2018
PubMed
Summary

This study introduces a hybrid model using artificial neural networks (ANN) and particle swarm optimization (PSO) for accurate liver disease classification. The novel approach enhances early disease detection and prognosis, improving patient outcomes.

Keywords:
Artificial neural networkClassificationLiver disordersParticle swarm optimizationSpark

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Early disease detection is crucial for effective medical treatment and patient prognosis.
  • Liver diseases, including Hepatitis B and C, are increasingly prevalent, necessitating accurate diagnostic tools.
  • Current automated liver disease classification systems often lack the accuracy of traditional methods like surgical biopsy.

Purpose of the Study:

  • To propose a novel hybrid model for accurate liver syndrome classification and early diagnosis.
  • To enhance the accuracy of automated medical diagnosis systems for liver disorders.
  • To develop an intelligent system for improved prognosis of liver diseases.

Main Methods:

  • A hybrid model combining M-PSO (Multi-Particle Swarm Optimization) for feature selection and M-ANN (Multi-Layer Artificial Neural Network) for classification was developed.
  • Patient medical data was analyzed to classify the possibility of disease existence.
  • The Spark tool was utilized for examining and evaluating the algorithm's performance.

Main Results:

  • The proposed hybrid model demonstrated significantly improved accuracy in liver disease classification compared to existing algorithms.
  • The M-PSO feature selection effectively identified relevant input variables for classification.
  • The M-ANN algorithm provided reliable classification of liver syndromes based on patient data.

Conclusions:

  • The developed hybrid approach offers a more accurate and efficient method for liver disease classification.
  • This intelligent system has the potential to aid clinicians in early diagnosis and prognosis of liver disorders.
  • The study highlights the effectiveness of integrating advanced data mining techniques for medical data analysis.