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

Early detection of any sort of disease is mandatory for effective medical treatment. Medical diagnosis relies heavily on Data Mining for automated disease classification and detection. It relies on data mining algorithms to examine medical data. Liver diseases have become more common these days with many new patients being diagnosed with Heptasis B and C. Early diagnosis of Liver Disorder is essential for treatment. It can be achieved by setting up intelligent systems for early diagnose and prognosis of Liver diseases. The existing automated classification systems lack accuracy in results when compared to surgical biopsy. We propose a new hybrid model for liver syndrome classification for analysis of the patient’s medical data via hybrid artificial neural network. The medical records are classified based on the possibility of existence of disease. The proposed method uses M-PSO for feature selection of input variables and M-ANN algorithm for disease classification. The presented hybrid approach significantly improves the accuracy compared to existing classification algorithms. The results of the algorithm were examined and evaluated using Spark tool in this work.