Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Unveiling NLR pathway signatures: EP300 and CPN60 markers integrated with clinical data and machine learning for precision NASH diagnosis

BACKGROUND

Given the increasing prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) and non-alcoholic steatohepatitis (NASH), there is a critical need for accurate non-invasive early diagnostic markers.

OBJECTIVE

This study aimed to validate NLRP3-related RNA signatures (EP300, CPN60, and ITGB1 mRNAs, miR-6881-5p, and LncRNA-RABGAP1L-DT-206) using an integrated molecular approach and advanced machine-learning algorithms to identify robust biomarkers for early diagnosis of NASH.

METHODS

A cohort of 237 participants (117 Healthy controls, 60 MAFLD, 120 NASH) was utilized. Twenty-five demographic, clinical, and molecular features were collected from each participant. Various machine learning models were trained on the dataset.

RESULTS

The Random Forest algorithm emerged as the most effective classifier. The model identified nine key features: EP300 mRNA, CPN60 mRNA, AST, D. bilirubin, Albumin, GGT, HbA1c, HOMA-IR, and BMI, achieving an impressive 97 % accuracy in distinguishing NASH from non-NASH cases.

CONCLUSION

The integration of molecular, clinical, and demographic data with machine learning algorithms provides a highly accurate method for the early diagnosis of NASH. This model holds promise for early detection in individuals at risk of progressing to cirrhosis or liver cancer and may aid in identifying new therapeutic targets for managing NASH.

Estimation of non-alcoholic steatohepatitis (NASH) disease using clinical information based on the optimal combination of intelligent algorithms for feature selection and classification

The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are 81.51 ± 9.35 , 82.53 ± 11.24 , 85.28 ± 9.68 , and 89.49 ± 7.92 , respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle.

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023)

BACKGROUND AND OBJECTIVES

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data.

METHODS

We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information".

RESULTS

We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches.

CONCLUSION

AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.

DNA content and clinicopathological features aid in distinguishing ameloblastic carcinoma from ameloblastoma

BACKGROUND

Ameloblastoma and ameloblastic carcinoma are epithelial odontogenic tumors that can be morphologically similar. In the present study, we evaluated the DNA content and Ki-67 index in the two tumors.

METHODS

The paraffin blocks of the tumors were selected to obtain sections for the immunohistochemical reactions and preparation of the cell suspension for acquisition in a flow cytometer. The Random Forest package of the R software was used to verify the contribution of each variable to classify lesions into ameloblastoma or ameloblastic carcinoma.

RESULTS

Thirty-two ameloblastoma and five ameloblastic carcinoma were included in the study. In our sample, we did not find statistically significant differences in Ki-67 labeling rates. A higher fraction of cells in 2c (G1) was correlated with the diagnosis of ameloblastoma, whereas higher rates of 5c-exceeding rate (5cER) were correlated with ameloblastic carcinoma. The Random Forest model highlighted histopathological findings and parameters of DNA ploidy study as important features for distinguishing ameloblastoma from ameloblastic carcinoma.

CONCLUSION

Our findings suggest that the parameters of the DNA ploidy study can be ancillary tools in the classification of ameloblastoma and ameloblastic carcinoma.

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning

OBJECTIVE

To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.

METHODS

371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors.

RESULTS

The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment.

CONCLUSIONS

ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

Mining association rules between the granulation feasibility and physicochemical properties of aqueous extracts from Chinese herbal medicine in fluidized bed granulation

Fluidized bed granulation (FBG) is a widely used granulation technology in the pharmaceutical industry. However, defluidization caused by the formation of large aggregates poses a challenge to FBG, particularly in traditional Chinese medicine (TCM) due to its complex physicochemical properties of aqueous extracts. Therefore, this study aims to identify the complex relationships between physicochemical characteristics and defluidization using data mining methods. Initially, 50 types of TCM were decocted and assessed for their potential influence on defluidization using a set of 11 physical properties and 10 chemical components, utilizing the loss rate as an evaluation index. Subsequently, the random forest (RF) and Apriori algorithms were utilized to uncover intricate association rules among physicochemical characteristics and defluidization. The RF algorithm analysis revealed the top 8 critical factors associated with defluidization. These factors include physical properties like glass transition temperature (Tg) and dynamic surface tension (DST) of DST100ms, DST1000ms, DST10ms and conductivity, in addition to chemical components such as fructose, glucose and protein contents. The results from Apriori algorithm demonstrated that lower Tg and conductivity were associated with an increased risk of defluidization, resulting in a higher loss rate. Moreover, DST100ms, DST1000ms and DST10ms exhibited a contrasting trend in the physical properties Specifically, defluidization probability increases when Tg and conductivity dip below 29.04℃ and 6.21 ms/m respectively, coupled with DST10ms, DST100ms and DST1000ms values exceeding 70.40 mN/m, 66.66 mN/m and 61.58 mN/m, respectively. Moreover, an elevated content of low molecular weight saccharides was associated with a higher occurrence of defluidization, accompanied by an increased loss rate. In contrast, protein content displayed an opposite trend regarding chemical properties. Precisely, the defluidization likelihood amplifies when fructose and glucose contents surpass 20.35 mg/g and 34.05 mg/g respectively, and protein concentration is less than 1.63 mg/g. Finally, evaluation criteria for defluidization were proposed based on these results, which could be used to avoid this situation during the granulation process. This study demonstrated that the RF and Apriori algorithms are effective data mining methods capable of uncovering key factors affecting defluidization.

Applying data mining techniques to predict vitamin D deficiency in diabetic patients

Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.

Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: Integrative analysis of eight clinical trials

BACKGROUND

Few models exist to predict mortality in cancer patients receiving immunotherapy. Our aim was to build a machine learning-based risk stratification model for predicting mortality in atezolizumab-treated cancer patients.

METHODS

Data from 2538 patients in eight atezolizumab-treated cancer clinical trials across three cancer types (non-small-cell lung cancer, bladder transitional cell carcinoma, and renal cell carcinoma) were included. The whole cohort was randomly split into development and validation cohorts in a 7:3 ratio. Machine-learning algorithms (extreme gradient boosting, random forest, logistic regression with lasso regularization, support vector machine, and K-nearest neighbor) were applied to develop prediction models. Model performance was mainly assessed by area under the receiver operating characteristic curve (AUC) value, calibration plot, and decision curve analysis. The probability of death risk was then stratified.

RESULTS

One thousand and three hundred and seventy-nine (54.33%) patients died. The random forest (RF) model was overall the best in terms of predictive performance, with the AUC of 0.844 (95% confidence interval [CI]: 0.826-0.862) in the development cohort and 0.786 (95% CI: 0.754-0.818) in the validation cohort for predicting mortality. Twelve baseline variables contributing to mortality prediction in the RF model were C-reactive protein, PD-L1 level, cancer type, prior liver metastasis, derived neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, hemoglobin, white blood cell count, number of metastatic sites, pulse rate, and Eastern Cooperative Oncology Group (ECOG) performance status. A total of 1782 (70.2%) patients were separated into the high-risk and 756 (29.8%) low-risk groups. Patients in the high-risk group were significantly more likely to die, experience disease progression, discontinue study, and discontinue treatment than patients in the low-risk group (all p values < 0.001). Risk groups were not associated with immune-related adverse events and grades 3-5 treatment-related adverse events (all p values > 0.05).

CONCLUSION

RF model has good performance in mortality prediction and risk stratification for cancer patients receiving atezolizumab monotherapy.

Artificial intelligence in nonalcoholic fatty liver disease

Background

Nonalcoholic fatty liver disease (NAFLD) has led to serious health-related complications worldwide. NAFLD has wide pathological spectra, ranging from simple steatosis to hepatitis to cirrhosis and hepatocellular carcinoma. Artificial intelligence (AI), including machine learning and deep learning algorithms, has provided great advancement and accuracy in identifying, diagnosing, and managing patients with NAFLD and detecting squeal such as advanced fibrosis and risk factors for hepatocellular cancer. This review summarizes different AI algorithms and methods in the field of hepatology, focusing on NAFLD.

Methods

A search of PubMed, WILEY, and MEDLINE databases were taken as relevant publications for this review on the application of AI techniques in detecting NAFLD in suspected population

Results

Out of 495 articles searched in relevant databases, 49 articles were finally included and analyzed. NASH-Scope model accurately distinguished between NAFLD and non-NAFLD and between NAFLD without fibrosis and NASH with fibrosis. The logistic regression (LR) model had the highest accuracy, whereas the support vector machine (SVM) had the highest specificity and precision in diagnosing NAFLD. An extreme gradient boosting model had the highest performance in predicting non-alcoholic steatohepatitis (NASH). Electronic health record (EHR) database studies helped the diagnose NAFLD/NASH. Automated image analysis techniques predicted NAFLD severity. Deep learning radiomic elastography (DLRE) had perfect accuracy in diagnosing the cases of advanced fibrosis.

Conclusion

AI in NAFLD has streamlined specific patient identification and has eased assessment and management methods of patients with NAFLD.

Meroterpenoids From Ganoderma lucidum Mushrooms and Their Biological Roles in Insulin Resistance and Triple-Negative Breast Cancer

Ganoderma fungi as popular raw materials of numerous functional foods have been extensively investigated. In this study, five pairs of meroterpenoid enantiomers beyond well-known triterpenoids and polysaccharides, dayaolingzhiols I−M (1–5), were characterized from Ganoderma lucidum. Their structures were identified using spectroscopic and computational methods. Structurally, compound 1 features a novel dioxabicyclo[2.2.2]octan-3-one motif in the side chain. Ethnoknowledge-derived biological evaluation found that (+)-5 could activate Akt and AMPK phosphorylation in insulin-stimulated C2C12 cells, and (+)-5 could activate glucose uptake dose dependently in C2C12 cells. Furthermore, we found that (+)-1 (+)-4, and (–)-4 could significantly inhibit cell migration of the MDA-MB-231 cell line, of which (+)-4 showed significant inhibitory effects against cell migration of the MDA-MB-231 cell line in a dose-dependent manner. These findings revealed the meroterpenoidal composition of G. lucidum and its roles in the prevention of chronic diseases such as diabetes mellitus and triple-negative breast cancer.