Classification of liver disorder from serum profile using extreme learning machine
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Date
2018-07
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
A problem is said to be well posed if it fulfilled the following properties: 1) a solution exists, 2) the solution is unique, and 3) non-perturbation. There exists a special case of the well posed problem where the inverse of the major function does not exist. The prediction of liver disorder in human from serum profile is one such problem. There are several diseases that could possibly affect the liver, with ample category of symptoms. In liver along with symptoms variation, intensity and severity differ from nearly insignificant to life-threatening. The proposed work predicts liver disorder in humans by finding relationship between serum profile parameters and occurrence of liver disorder using machine learning approach. Extreme Learning Machine (ELM) has enough capabilities to solve this type of problem. Extreme Learning Machine approach is utilized for implementing the proposed work by predicting liver disorder in human using serum profile. The serum profile considered in the research work is Tb, Alkphos, Sgpt, Sgot and others. Model classifies on the basis of disease severity. The datset utilized in the thesis is compiled from the UCI machine learning repository. Model is evaluated on several samples of dataset utilizing confusion matrixes and ROC curves for different activation functions. Based on that accuracy of the model evaluation is performed and RBF found to be best activation function to be used in the liver disorder problem. Proposed model has an accuracy of 80.9% for Radial Basis Function.
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