Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques

Singh, Jagdeep; Bagga, Sachin; Kaur, Ranjodh

doi:10.1016/j.procs.2020.03.226

Cited by 75 publications

(28 citation statements)

References 12 publications

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“…It is observed that the decision algorithm has the highest precision, recall and f1-score as well with 92%, 99% and 95.3% respectively. [22] 2019 ILPD LG 72.50 % Thaiparnit et al [23] 2018 Liver Disorder RF 75.76 % Rahman et al [24] 2019 ILPD LG 75% Kumar and Thakur [25] 2020 BUPA, ILPD Fuzzy-NWKNN 78.46% Rabbi et al [26] 2020 ILPD AdaBoost 92.19% Poonguzharselvi et al [27] 2021 UCI repository Random Forest 84%…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…To quantify the effect of feature selection, accuracy is used for measuring the performance. The accuracy for SVM is determined by using the formula given in (1),…”

Section: Liver Disease Dataset Analysismentioning

confidence: 99%

“…Liver disease is one of the leading causes of mortality worldwide and constitutes a wide range of diseases with varied or unknown etiologies. For instance, a study shows that in 2017 1.32 million deaths worldwide, or 2 to 4% of all annual deaths were caused directly due to cirrhosis [1]- [3]. With the help of automated decision-making methods using a machine learning model, the death caused due to liver disease can be reduced.…”

Section: Introductionmentioning

confidence: 99%

Random forest and support vector machine based hybrid liver disease detection

Assegie¹,

Rajkumar

Kumar

et al. 2022

Bulletin EEI

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This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.

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“…The ML methods described in previous studies have been evaluated for accuracy by a combination of confusion matrix, receiver operating characteristic under area under curve, and k-fold cross-validation. Singh et al designed software based on classification algorithms (including logistic regression, random forest, and naive Bayes) to predict the risk of liver disease from a data set with liver function test results [35]. Vijayarani and Dhavanand found that SVM performed better over naive Bayes to predict cirrhosis, acute hepatitis, chronic hepatitis, and liver cancers from patient liver function test results [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical Machine Learning Approaches to Liver Disease Prediction

Mostafa

Hasan

Williamson

et al. 2021

Livers

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Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.

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Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques

Cited by 75 publications

References 12 publications

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

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

Random forest and support vector machine based hybrid liver disease detection

Statistical Machine Learning Approaches to Liver Disease Prediction

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