Abstract:
Steatosis, often known as fatty liver disease, is a common disorder brought on by an
overgrowth of fat in the liver. There is a modest amount of fat in a healthy liver. A
problem arises when fat makes up 5% to 10% or more of the weight of our liver. This
illness is linked to a high risk of morbidity and mortality. The goal of our research
proposal is to create a machine learning model that can help physicians identify high-risk
patients, provide a distinct diagnosis, and prevent and treat FLD. 55 of the 105 people
who were recruited had their FLD assessed. The sickness was found using classifier
models. Among the models available are decision trees, K-Neighbors classifiers, logistic
regressions (LR), random forests (RF), and linear regressions and Decision Tree
Classifier. The link between the model's actual and anticipated values was examined
using the confusion matrix. According to the study's findings, machine learning
algorithms can accurately predict FLD. According to our research, the Logistic
Regression model is the most reliable regression model. The Random-Forest-Classifier
has a higher accuracy of 68 percent than the other classification models. Our method
could be used to find FLD patients who could have a big impact on how treatments are
delivered. The use of this algorithm for early prediction could lower medical expenses
and save money on treatment.
