An efficient machine learning model for classification of liver patient diseases

Data mining methods/techniques are playing a very important role in healthcare these days. Due to changed lifestyle of people diseases are becoming common. Among these diseases Liver related diseases are one of the fastest growing ones. Many people have done their bit in this field by classifying data and they have applied multiple various classification techniques like Decision Tree, Support Vector Machine, Naïve Bayes, Artificial Neural Network (ANN) etc. to classify the data. These techniques can be very useful in time bound and accurate classification and hence prediction of diseases can be much more easy which in turn leads to better care of patients. In this research work, we have provided a comparative analysis of various classification methods over given data set taken from UCI repository. We have improved the accuracy of prediction by applying preprocessing, feature selection and performing many other methods over data. Out of all the classification techniques Random forest with recursive feature elimination and feature selection technique has the highest accuracy.