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...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 11; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132813847&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132813847 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2018 vid: 42 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132813847 132813847 132813847 10.1007/s10916-018-1073-8 132813847 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: HANN: A Hybrid Model for Liver Syndrome Classification by Feature Assortment Optimization. aug: au: Anand, L. Syed Ibrahim, S. P. affil: School of Computing Science and Engineering, VIT, Chennai, Tamil Nadu, India sug: subj: Liver Diseases Classification Neural Networks (Computer) Data Mining Algorithms Liver Diseases Diagnosis Liver Diseases Prognosis Image Processing, Computer Assisted Decision Support Systems, Clinical ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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