Performance Evaluation of Machine Learning Techniques for the Classification of BUPA Liver Disorder
DOI:
https://doi.org/10.26438/ijcse/v7i2.864869Keywords:
artificial neural Networks, ANN, classification accuracy, CA, backward elimination, BE, CAentropy evaluation (EE), feature subset selection methods, FSM’s; forward selection, FS; logistic regressionAbstract
Liver is an important organ which plays major role in digesting food, removing poisons and stocking energy. One major challenge is to identify the Liver disorder using its ambiguous symptoms due to this many people’s are suffering like anything. So to overcome the challenges we have proposed a method to identify the disorder which in turn will help medical field and society. Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Technically the data mining can be considered as the sequence of steps followed for searching patterns or identifying correlations between large numbers of fields within a huge relational database. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Data mining techniques are applied to different medical domains to improve the medical diagnosis. Improving the accuracy of the classification and improving the prediction rate of medical datasets are the main tasks/challenges of medical data mining. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the performances of machine learning techniques like Logistic Regression (LR), Artificial Neural Networks (ANN), and ANN with k-fold Cross Validation Sample (CVS) with Feature Selection Methods (FSMs) using Percentage Split (PS) as test option on Liver Disorder Datasets. The performance of the proposed model is measured in the form of classification accuracy. Performance of proposed work is assessed as classification accuracy. The work deliver the better accuracy for reduced set of attributes compared with full set attribute and we state that those are the very important tests compared to all tests to identify the disorder.
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