NEW ANALYSIS LIVER DISEASE DIAGNOSIS USING LOGISTIC REGRESSION

Abstract

ABSTRACT:   Liver disease has become a prominent global health concern, with conditions like cirrhosis and liver cancer ranking among the leading causes of mortality worldwide. We are going discuss how to predict risk of liver disease for a person, based on the blood test report results of the user. The major goal of this study is to employ classification algorithms to distinguish between liver patients and healthy people. Chemical components present in the human body, as well as tests such as SGOT and SGPT, determine whether a person is a patient, or whether they need to be diagnosed. We proposed apply machine learning algorithms to check the entire patient’s liver disorder. Chronic liver disorder is defined as a liver disorder that lasts for at least six months. In this regard, this study provides an extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research. These are decision trees, random forests, and logistic regressions. Accuracy, specificity, sensitivity, and the area under the receiver operating characteristic (ROC) curve are the metrics that are used in order to assess the performance of these models. The programming language which was used is python and machine learning Sklearn was used to build the model using classification algorithms like Logical regression, SVM. A data-driven technique that makes use of supervised learning algorithms is presented in this research report as a method for estimating the likelihood of developing liver disease. Subsequentaluation of the Five algorithms for machine learning, restated. The goal of this research is to analyse prediction algorithms in order to relieve doctors of their workload.  Thus, the outputs of the proposed classification model show accuracy in predicting the result