Statistical Analysis and Identification of Important Factors of Liver Disease Using Machine Learning and Deep Learning Architecture

Abstract

One of the most prominent and metabolically the most active organ of human body is liver, whose formation is built upon subtle organic compounds and responsible for storing the energy of a living body. Damaging this organ could lead to liver failure and possibly imply to a gruesome death. An early detection of primary causes of liver failure could revoke the adverse effect of liver diseases. Several metabolic compounds of human body such as Bilirubin, Total Proteins, Albumin, Alkaline, Alamine, Asparatate and outer factors such as gender, age etc. are considered as vital elements to find possible liver disease patient. In this paper, we have attempted to analyze the causal factors behind liver disease statistically and bring out the most significant factors. Again, we have introduced a reduced model based on the significance of logistic regression analysis, where we successfully eliminated highly co-related variables to get rid of multi co-linearity. A comparison has been conducted on the basis of the performance of the various machine learning and deep learning algorithms.