Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 9
Applications of Machine Learning for Prediction of Liver Disease
Khan Idris, Sachin Bhoite
10.7753/IJCATR0809.1012
keywords : Indian Liver Patients, Machine Learning, Logistic regression, Support Vector Machine, Random Forest, AdaBoost, Bagging.
Patients in India for liver disease are continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. It is expected that by 2025 India may become the World Capital for Liver Diseases. The widespread occurrence of liver infection in India is contributed due to deskbound lifestyle, increased alcohol consumption and smoking. There are about 100 types of liver infections. Therefore, building a model that will help doctors to predict whether a patient is likely to have liver diseases, at an early stage will be a great advantage. Diagnosis of liver disease at a preliminary stage is important for better treatment. We also compare different algorithms for the better accuracy.
@artical{k892019ijcatr08091012,
Title = "Applications of Machine Learning for Prediction of Liver Disease",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="394 - 396",
Year = "2019",
Authors ="Khan Idris, Sachin Bhoite"}
Paper is based on the applications of machine learning used for prediction of liver diseases at an early stage.
Different types of machine learning algorithms are used and their accuracies are compared.
As Logistic Regression is a simple algorithm and takes less computing time and we got high accuracy by using Logistic Regression.
We, conclude that by using Logistic Regression with Adaboost we have gained highest accuracy of 74.36%.