Classification Of Liver Disease Research Articles

Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.

Improved Kepler Optimization Algorithm for enhanced feature selection in liver disease classification

Liver diseases represent a significant healthcare challenge, impacting millions globally and posing complexities in diagnosis. To address this global health concern, this paper introduces a groundbreaking enhancement to the Kepler Optimization Algorithm, termed I-KOA, designed specifically for feature selection in high-dimensional datasets. By harnessing the synergies of Opposition-Based Learning and a Local Escaping Operator grounded in the k-nearest Neighbor (kNN) classifier, I-KOA asserts itself as a potent tool for local exploitation, balanced exploration, and evasion of local optima. To our knowledge, this is the first work to exploit KOA as a feature selection method. Pioneering the utilization of KOA as a feature selection method, the paper rigorously tests I-KOA in two extensive experiments, tackling the complex CEC’22 benchmark suite functions and the intricate landscape of five liver disease datasets. Results underscore I-KOA’s unparalleled performance, validated through the Friedman test, where it surpasses seven rival optimization algorithms. Achieving an outstanding overall classification accuracy of 93.46%, Feature selection size of 0.1042, sensitivity of 97.46%, precision of 94.37%, and F1-score of 90.35% across the liver disease datasets, I-KOA’s randomized algorithm ensures robust feature selection, striking a compelling balance between subset size and classification efficacy. Acknowledging computational demands and generalization nuances, I-KOA is a formidable tool ready to revolutionize medical diagnosis and decision support systems. The open source codes of the proposed I-KOA are available at https://www.mathworks.com/matlabcentral/fileexchange/161376-improved-kepler-optimization-algorithm.

MASLD, MAFLD, or NAFLD criteria: have we re-created the confusion and acrimony surrounding metabolic syndrome?

In 1980, there was the first description of patients with nonalcoholic steatohepatitis (NASH), most of whom were overweight and had type 2 diabetes. In the following years, there has been a growing appreciation that metabolic dysfunction underpins this liver disease, and metabolic dysfunction also contributes to the increased risk of extrahepatic complications, manifest in nonalcoholic fatty liver disease (NAFLD) as a multisystem disease. In 2020 & 2023, it was proposed that NAFLD should be renamed and reclassified as metabolic dysfunction-associated fatty liver disease (MAFLD) or metabolic dysfunction-associated steatotic liver disease (MASLD), respectively. Despite subtle differences between MAFLD and MASLD, there is excellent congruence between NAFLD, MAFLD, and MASLD definitions, and affected patients usually meet the criteria for all. The following is a perspective of the authors’ views as to the challenges and advantages of the new fatty liver disease terminology and classification.

Liver Disease Classification - An XAI Approach to Biomedical AI

Liver Disease Classification - An XAI Approach to Biomedical AI

Comparison of NAFLD, MAFLD and MASLD characteristics and mortality outcomes in United States adults.

Following the classification of metabolic dysfunction-associated fatty liver disease (MAFLD), non-alcoholic fatty liver disease (NAFLD) has recently been redefined again as metabolic dysfunction-associated steatotic liver disease (MASLD). However, the distinctions in characteristics and mortality outcomes between NAFLD, MAFLD and MASLD remain unclear. We analysed data from 7519 participants in the third National Health and Nutrition Examination Surveys of United States (US) and their linked mortality until 2019. Survey weight-adjusted multivariable Cox proportional model was used to study the mortality over three terms. The prevalence of NAFLD, MAFLD and MASLD was 18.5%, 19.3% and 20.8%, respectively. Most individuals with NAFLD (94.5%) or MAFLD (100%) can be classified as MASLD, while a relatively low percentage of those with MASLD were also diagnosed with either NAFLD (84.1%) or MAFLD (92.7%). During a median follow-up of 26.9 years, both MAFLD and MASLD were associated with increased risk of all-cause mortality (adjusted hazard ratio [aHR] 1.18, 95% CI 1.04-1.33 and 1.19, 1.06-1.34, respectively), this association was mainly observed in NAFLD-/MASLD+ subgroups. NAFLD was not associated with all-cause mortality. However, all three terms were associated with an increased risk of all-cause mortality in individuals with advanced fibrosis (aHR: 1.71-1.81). Subgroup analyses showed that higher risk of all-cause mortality for both MAFLD and MASLD were observed among older adults (≥65 year), non-Hispanic whites and those without diabetes. Both MASLD and MALFD were linked to higher all-cause mortality risk, but MASLD identified a greater number of individuals compared to MAFLD.

Comparison of 3 Different Therapeutic Particles in Radioembolization of Locally Advanced Intrahepatic Cholangiocarcinoma.

Our objective was to compare 3 different therapeutic particles used for radioembolization in locally advanced intrahepatic cholangiocarcinoma. Methods: 90Y-glass, 90Y-resin, and 166Ho-labeled poly(l-lactic acid) microsphere prescribed activity was calculated as per manufacturer recommendations. Posttreatment quantitative 90Y PET/CT and quantitative 166Ho SPECT/CT were used to determine tumor-absorbed dose, whole-normal-liver-absorbed dose, treated-normal-liver-absorbed dose, tumor-to-nontumor ratio, lung-absorbed dose, and lung shunt fraction. Response was assessed using RECIST 1.1 and the [18F]FDG PET-based change in total lesion glycolysis. Hepatotoxicity was assessed using the radioembolization-induced liver disease classification. Results: Six 90Y-glass, 8 90Y-resin, and 7 166Ho microsphere patients were included for analysis. The mean administered activity was 2.6 GBq for 90Y-glass, 1.5 GBq for 90Y-resin, and 7.0 GBq for 166Ho microspheres. Tumor-absorbed dose and treated-normal-liver-absorbed dose were significantly higher for 90Y-glass than for 90Y-resin and 166Ho microspheres (mean tumor-absorbed dose, 197 Gy for 90Y-glass vs. 73 Gy for 90Y-resin and 50 Gy for 166Ho; mean treated-normal-liver-absorbed dose, 79 Gy for 90Y-glass vs. 37 Gy for 90Y-resin and 31 Gy for 166Ho). The whole-normal-liver-absorbed dose and tumor-to-nontumor ratio did not significantly differ between the particles. All patients had a lung-absorbed dose under 30 Gy and a lung shunt fraction under 20%. The 3 groups showed similar toxicity and response according to RECIST 1.1 and [18F]FDG PET-based total lesion glycolysis changes. Conclusion: The therapeutic particles used for radioembolization differed from each other and showed significant differences in absorbed dose, whereas toxicity and response were similar for all groups. This finding emphasizes the need for separate dose constraints and dose targets for each particle.

Implementation of ADABOOST Algorithm on C50 for Improving the Performance of Liver Disease Classification

The liver is a vital organ that is important for humans because it plays a role in regulating hormone cycles, neutralizing toxins, and controlling the composition of blood. Liver disease is a common ailment worldwide. Often, this disease occurs without specific symptoms (asymptomatic). Therefore, liver disease is known as a "silent killer," and it is necessary to quickly and accurately diagnose and treat liver diseases. Data mining technology can be useful for rapidly detecting liver diseases from laboratory diagnosis results. One classification algorithm that can be used is the C50 algorithm. This algorithm is an improvement over the previous C45 algorithm, with several advantages such as efficient memory usage and more concise tree results. However, the C50 algorithm may experience overfitting on complex medical data, requiring the boosting process using AdaBoost. The AdaBoost algorithm can make the C50 algorithm more susceptible to overfitting. Another advantage of the AdaBoost algorithm is its ability to handle imbalanced datasets in terms of target labels. This research used 583 data from UCI machine learning with two target labels: liver and normal. The research results show that the C50 algorithm is capable of identification with an accuracy rate of 74.58%. Furthermore, the C50 algorithm's accuracy can be maximized by AdaBoost to reach 86.44%

Breaking new ground: MASLD vs. MAFLD-which holds the key for risk stratification?

The classification and nomenclature of non-alcoholic fatty liver disease (NAFLD) has been the subject of ongoing debate in the medical community. Through the introduction of metabolic dysfunction-associated fatty liver disease (MAFLD) and the later release of metabolic dysfunction-associated steatotic liver disease (MASLD), the limitations associated with NAFLD are intended to be addressed. Both terminologies incorporate the metabolic component of the disease by providing diagnostic criteria that relies on the presence of underlying metabolic risk factors. An epidemiologic cross-sectional study of individuals who had undergone abdominal ultrasound and vibration-controlled transient elastography (VCTE) as part of a routine check was performed. We evaluated clinical, anthropometric, and biochemical variables to determine the metabolic profile of each subject. The study included a total of 500 participants, 56.8% (n = 284) males and 43.2% (n = 216) females, with a mean age of 49 ± 10years. 59.4% (n = 297) were diagnosed with MAFLD and MASLD, 10.2% (n = 51) were diagnosed only with MASLD and 30.4% (n = 152) were not diagnosed with either MAFLD or MASLD. The differences in prevalence were mainly based on the detection of individuals with a BMI < 25kg/m2, where MASLD captures the largest number (p < 0.001). Although MASLD has a higher capture of lean patients compared to MAFLD, patients with MAFLD and MASLD have a worse metabolic profile than those with only MASLD. Our results provide evidence that MAFLD better identifies patients likely to have a higher risk of liver fibrosis and of disease progression.

Improving nonalcoholic fatty liver disease classification performance with latent diffusion models

Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of 1.90 compared to 1.67 for GANs, and a minimum FID score of 69.45 compared to 100.05 for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of 0.904 on a NAFLD prediction task.

RCNN with Swallow Swarm Optimization for Liver Disease Detection and Classification

One of the most serious medical conditions that can endanger a person's life and health is liver disease. The second-leading cause of death for men and sixth-leading cause of death for women, respectively, is liver cancer. In 2008, liver cancer claimed the lives of almost 750,000 people, killing 960,000 of them. The segmentation and identification of CT images produced by computer tomography has emerged as a major topic in medical image processing. There are few choices for liver segmentation due to the enormous amount of time and resources necessary to train a deep learning model. As part of this research, we created the Region utilizing Convolutional Neural Network, a novel way of extracting the liver from CT scan images (RCNN). The suggested CNN approach, which employs softmax to isolate the liver from the background, contains of three convolutional layers and two entirely associated layers. In the CapsNet and CAL layers, there are class dependencies and an efficient mechanism to connect CAL and subsequent CapsNet processing. Finally, the classification is carried out using the SSO-CSAE model, an approach known as the swallow swarm optimization that is based on the Convolutional Sparse Autoencoder (CSAE) The MICCAI SLiver '07, 3Dircadb01, and LiTS17 benchmark datasets were used to validate the proposed RCNN-SSO approach. When compared to other frameworks, the proposed framework performed well in numerous categories.

Efficient Hybrid CNN Method to Classify the Liver Diseases

This study focuses on classifying liver diseases using dynamic CT scan images and deep learning techniques. The primary objective is to develop accurate and efficient models for distinguishing between different liver disease categories. Three deep learning models, ResNet50, ResNet18, and AlexNet, are employed for three-class classification, including Hepatitis/cirrhosis, Hepatitis/Fatty liver, and Hepatitis/Wilson's Disease. The dataset comprises dynamic CT scan images of the liver, each manually segmented to identify lesions. To enhance model performance, the data is pre-processed by resizing, normalization, and data augmentation. The dataset is split into training, validation, and test sets for model evaluation. The performance of each model is assessed using confusion matrices, accuracy, sensitivity, and specificity. Results show varying accuracies for different liver disease classes, indicating the strengths and limitations of the models. To overcome the limits of the three-class classifiers, a framework for the Efficient Hybrid CNN method to classify Liver diseases (EHCNNLD) is proposed, combining the predictions from the three models with weighted probabilities. The Proposed EHCNNLD method demonstrates improved accuracy and classification power, enhancing the overall performance for liver disease classification. The study highlights the potential of deep learning techniques in medical image analysis and clinical diagnosis. The findings provide valuable insights into developing robust and accurate models for liver disease classification, paving the way for medical research and patient care advancements.

Nonalcoholic fatty liver disease. New nomenclature. Review

At the international hepatological congress of the European Association for the Study of the Liver (EASL), which took place in Vienna on June 21 — 24, 2023, a new classification and nomenclature of fatty liver disease was adopted. Leading scientists‑hepatologists proposed to replace the term «non‑alcoholic fatty liver disease» to the term «metabolic dysfunction‑associated steatotic liver disease» (MASLD). This diagnosis is established in patients with confirmed hepatic steatosis and one of the five cardiometabolic risk factors. MetALD is a new category of steatosis defined in patients with MASLD who consume excessive amounts of alcohol per week (140 and 210 g/ week for women and men, respectively). Cryptogenic steatohepatitis is present in patients with an undetermined disease etiology of the disease or without any metabolic risk factors. The concept of «non‑alcoholic steatohepatitis» (NASH) has been changed to the concept of «metabolic dysfunction‑associated steatohepatitis» (MASH). In the 21st century the problem of diagnosis and treatment of non‑alcoholic fatty liver disease has become especially urgent since it occurs in almost 35% of the population of the planet Earth. In industrialized countries, the percentage of the population affected with this disease is 20 to 40%. Approximately 10 — 20% of patients have associated symptoms of NASH. In Western European countries, the prevalence of NASH is 2 — 6%, and in the USA it is as much as 5 — 6% of the total population. MASH is the most common chronic liver disease that does not depend on age or race. This pathology is directly related to obesity, type 2 diabetes mellitus, insulin resistance, hyperlipidemia, and atherosclerosis. NAFLD is diagnosed 3 — 5 times more often in men than in women. Gender differences are related to the fact that body mass index of men is slightly higher than that in women. The fact that men with this disease often consume more alcohol than women, plays a certain role, too.

An Enhanced Approach to Liver Disease Classification: Implementing Convolutional Neural Network with Attention Layer Gated Recurrent Unit

An Enhanced Approach to Liver Disease Classification: Implementing Convolutional Neural Network with Attention Layer Gated Recurrent Unit

LIVER DISEASE CLASSIFICATION ANALYSIS USING THE XGBOOST METHOD

Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data. Keywords: Xgboost, Liver, Classification, Disease

Metabolic dysfunction-associated fatty liver disease increases risk of chronic kidney disease: a systematic review and meta-analysis

Background and aimMetabolic dysfunction-associated fatty liver disease (MAFLD) is an alternative description and classification of non-alcoholic fatty liver disease (NAFLD) that may have better utility than NAFLD in clinical practice....

A fatty liver diseases classification network based on adaptive coordination attention with label smoothing

Accurate classification of fatty liver disease (FLD) from fuzzy boundary areas and lesion areas in ultrasound imaging is crucial for clinical diagnosis and treatment. This paper presents an efficient convolutional neural network based on adaptive coordinated attention (ACA), which combines label smoothing and EfficientNet for FLD classification. We only uses pixels and labels of original brightness mode ultrasound images as inputs, and achieves four classification of FLD. It avoids the decoupling of feature extraction and classification, and achieves end-to-end automatic classification identification. Based on ACA, the model can adaptively encode lateral and vertical positional information adaptively into channel attention that adaptively learns information in both spatial directions to enhance the representation of the objects of concern. Furthermore, the interpretability of the model is demonstrated through feature heatmap and hybrid heatmap visualization techniques. The experimental results reveal that the proposed approach attains an accuracy of 94.96% and an AUC of 0.97 on the private dataset. The F1 scores for the four categories are 0.963, 0.942, 0.932, and 0.955, respectively, contributing to an overall F1 score of 0.955, surpassing other baseline models and achieving a new state-of-the-art (SOTA). Meanwhile, this approach also achieves SOTA accuracy of 97.82% on public datasets. The proposed approach has surpassed the manual recognition of fatty liver brightness mode ultrasound images in terms of its cognitive ability, which helps to aid the expert diagnosis. It has better performance and achieves end-to-end classification compared to other methods.

Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm

Diagnosing liver disease in the field of healthcare is not an easy task. However, by utilizing medical records as datasets and applying data mining methods such as K-Nearest Neighbor (K-NN), we can analyze and extract knowledge automatically. The K-NN method has proven to be more effective compared to other methods as it clusters new information by selecting the nearest neighbors based on the value of k. In this study, we employed the Elbow method to determine the optimal value of k by measuring the error rate. The test results revealed that the optimal value of k was found to be 4, with the lowest error rate. In the third test, we achieved a training accuracy of 80.5% and a testing accuracy of 78.9%. After fine-tuning the training data, we were able to improve the accuracy to 82.2% for training and 77.1% for testing. However, in the fourth test, we encountered overfitting issues due to data imbalance caused by inappropriate resampling, resulting in a model that was overly complex and prone to excessive noise.

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images.

To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

Is fatigue related to the severity of liver inflammation in patients with chronic liver disease? A cross-sectional study

ObjectivesFatigue is common in patients with chronic liver disease; however, its pathogenesis is unclear. This study aimed to provide insights into the pathogenesis of chronic liver disease-related fatigue by assessing...

Computative Analysis of Various Techniques for Classification of Liver Disease

This paper presents a software-engineered approach using a classification algorithm for the classification of liver disease. The ILPD dataset is used for the proposed work. Different attributes of liver patient records such as direct bilirubin, age, sex, total bilirubin, alphos, albumin, sgpt, globulin ratio, sgot are used to classify liver disease. The proposed Convolution Neural Network classification technique shows an accuracy of 67% and a precision of 71%. Various classification algorithms such as CNN, RNN, ANN, and logistic regression are executed on the liver patient dataset and their accuracy is determined.