Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty, Sivala Vishnu; Kumar, R Kiran

doi:10.35940/ijeat.f8684.088619

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…The accuracies of these modelling techniques were found to be above 85% and majority of them were 90% percent and above. One of the papers by Murty and Kumar (2019) showed that with the optimization of L2 regularization, Logistic loss function, learning rate and the number of estimators that are used in the model development gave them an accuracy of 99% which is the highest recorded in the previously done studies. Budoliya, Shrivastava and Sharma (2020) used the Bayesian Optimization on their model and managed to get an 85% accuracy on the training dataset and a 91.8% accuracy on the testing dataset.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 93%

Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models

Singh

Ponnusamy

Ling

et al. 2022

Preprint

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Systematic Lupus Erythematosus (SLE) is an irreversible autoimmune disease that has seen to bring a lot of negative effect on the human body. It has become a very challenging task in predicting the prevalence of Lupus in patients. It has slowly gained popularity among many researchers to study the prevalence of this disease and developing prediction models that not only study the prevalence of the disease but is also able to predict suitable dosage requirements, treatment effectiveness and the severity of the disease in patients. All of these is usually done with medical records or clinical data that has different attributes related and significant to the analysis done. With the advancement in machine learning models and ensemble techniques, accurate prediction models have been developed. However, these models are not able to explain the significant contributing factors as well as correctly classify the severity of the disease. Decision Tree Classifier, Random Forest Classifier and Extreme Gradient Boosting (XGBoost) are the models that will be used in this paper to predict the early prevalence to Lupus Disease in patients using clinical records. The most significant factors affecting Systematic Lupus Erythematosus (SLE) will then be identified to aid medical practitioners to take suitable preventive measures that can manage the complications that arise from the disease. Hence, this paper aims to assess the performance of tree models by performing several experiments on the hyper parameters to develop a more accurate model that is able to classify Lupus Disease in patients in the early stages. Findings revealed that the best model was the Random Forest Classifier with parameter tuning. The most significant factor that affected the presence of Lupus Disease in patients was identified as the Ethnicity and the Renal Outcome or the kidney function of the patients.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 93%

Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models

Singh

Ponnusamy

Ling

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A back-propagation network (BPN) combined with a multilayer feed-forward deep neural network (MLFFDNN) was utilized by [22]. XGBoost was used to estimate liver disease data, and the authors used L1 and L2 [23] during their work. An imbalance in ILPD was handled through a specific method called the minority oversampling algorithm.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Nisa

Buzdar

Khan³

et al. 2022

Symmetry

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Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

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Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

Dhiya'ulhaq,

Safira,

Fahmiyah

et al. 2024

MethodsX

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Accurate Liver Disease Prediction with Extreme Gradient Boosting

Cited by 8 publications

References 14 publications

Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models

Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

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