An Efficient Prediction Model for Liver Disorder Database using Data mining Techniques

Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, predictions, forecasting and estimation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information for effective decision making. This research has developed a Decision Support in Liver Disorder Prediction System using various data mining modeling techniques, like, Decision tree, Naive Bayes, SVM, KNN, Random Forest algorithm to classify these diseases and compare the effectiveness, correction rate among them. Detection of Liver disease in its early stage is the key of its cure. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy, diminishing of computing time needed to build models as they learn faster, and better understanding of the models. The predictive performances of popular classifiers are compared quantitatively.