Ensembled Correlation Between Liver Analysis Outputs
This work addresses liver disease prediction for healthcare applications, but it is incremental as it applies standard data mining methods to a new dataset.
The study tackled the problem of identifying correlations between liver disorder and liver analysis outputs using data mining techniques on a dataset of 16,380 results, finding correlations with up to 94% correlation coefficient and down to 15% error rate.
Data mining techniques on the biological analysis are spreading for most of the areas including the health care and medical information. We have applied the data mining techniques, such as KNN, SVM, MLP or decision trees over a unique dataset, which is collected from 16,380 analysis results for a year. Furthermore we have also used meta-classifiers to question the increased correlation rate between the liver disorder and the liver analysis outputs. The results show that there is a correlation among ALT, AST, Billirubin Direct and Billirubin Total down to 15% of error rate. Also the correlation coefficient is up to 94%. This makes possible to predict the analysis results from each other or disease patterns can be applied over the linear correlation of the parameters.