Supervised machine learning based liver disease prediction approach with LASSO feature selection

Abstract

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.

Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation

“…A more direct approach would be the application of Lasso or Ridge regression to determine the coefficients of the variables and lower them to zero to eliminate the low-influenced ones from the dataset [29]. Still, RF does have some benefits in comparison to these methods such as robustness to non-linearity and multicollinearity, lower sensitivity to outliers and unscaled features, and automatic variable interaction capturing [30,31].…”

Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset

“…A hyperplane is a function that best separates classes, and a margin is the distance between the two nearest data points of distinct classes to the hyperplane. These models learn to categorize, forecast, and find outliers [24], [25].…”

Random forest and support vector machine based hybrid liver disease detection

“…In addition, Afrin et al [11] conducted a comparative study on the prediction of liver disease using SVM and an Adaptive boosting algorithm. The SVM and an adoptive boosting algorithm are trained using 583 samples of liver disease dataset collected from the University of California Irvine (UCI) data dataset.…”