Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis
Background and Objective:: Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dys...
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Elsevier
2024-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990024000326 |
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| author | Deepak Kumar Brijesh Bakariya Chaman Verma Zoltán Illés |
| author_facet | Deepak Kumar Brijesh Bakariya Chaman Verma Zoltán Illés |
| author_sort | Deepak Kumar |
| collection | DOAJ |
| description | Background and Objective:: Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics. Method:: One popular and effective approach is collecting serum biomarker samples. The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). Models’ accuracy was assessed using K-Fold Cross-Validation (CV).Distinct pattern were identified using Latent Semantic Analysis(LSA). Furthermore, SHAP plots were utilized for enhanced interpretability, highlighting essential features for liver dysfunction levels. Results:: The inflammatory profile, mixed disease profile, and healthy profile were the three distinct clusters were identified with LSA. The RF model achieved high accuracy of 0.94±0.06. Serum Glutamate Pyruvate Transaminase (GPT), Age at Diagnosis (AAD), Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP) were found the most key important features in liver disease staging increment. Conclusion:: The research significantly contributes to the fields of biomedical informatics and clinical decision-making. The developed model offers valuable decision-making tools for clinicians, enabling early and targeted interventions. |
| format | Article |
| id | doaj-art-b65051657dcc4a18a5b38175223e2f03 |
| institution | OA Journals |
| issn | 2666-9900 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-b65051657dcc4a18a5b38175223e2f032025-08-20T01:59:04ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002024-01-01610016510.1016/j.cmpbup.2024.100165Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysisDeepak Kumar0Brijesh Bakariya1Chaman Verma2Zoltán Illés3Department of Computer Application, IKGPTU, Jalandhar, 144603, Punjab, IndiaDepartment of Computer Application, IKGPTU, Jalandhar, 144603, Punjab, IndiaDepartment of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053, Budapest Hungary; Corresponding author.Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053, Budapest HungaryBackground and Objective:: Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics. Method:: One popular and effective approach is collecting serum biomarker samples. The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). Models’ accuracy was assessed using K-Fold Cross-Validation (CV).Distinct pattern were identified using Latent Semantic Analysis(LSA). Furthermore, SHAP plots were utilized for enhanced interpretability, highlighting essential features for liver dysfunction levels. Results:: The inflammatory profile, mixed disease profile, and healthy profile were the three distinct clusters were identified with LSA. The RF model achieved high accuracy of 0.94±0.06. Serum Glutamate Pyruvate Transaminase (GPT), Age at Diagnosis (AAD), Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP) were found the most key important features in liver disease staging increment. Conclusion:: The research significantly contributes to the fields of biomedical informatics and clinical decision-making. The developed model offers valuable decision-making tools for clinicians, enabling early and targeted interventions.http://www.sciencedirect.com/science/article/pii/S2666990024000326Latent Semantic AnalysisMachine LearningLiver risk predictionFeature importanceClinical decision-making |
| spellingShingle | Deepak Kumar Brijesh Bakariya Chaman Verma Zoltán Illés Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis Computer Methods and Programs in Biomedicine Update Latent Semantic Analysis Machine Learning Liver risk prediction Feature importance Clinical decision-making |
| title | Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis |
| title_full | Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis |
| title_fullStr | Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis |
| title_full_unstemmed | Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis |
| title_short | Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis |
| title_sort | deciphering the complex links between inflammatory bowel diseases and nafld through advanced statistical and machine learning analysis |
| topic | Latent Semantic Analysis Machine Learning Liver risk prediction Feature importance Clinical decision-making |
| url | http://www.sciencedirect.com/science/article/pii/S2666990024000326 |
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