A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease
Background: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. Objective: The aim of our observational study was to build a machine lear...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-11-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024148797 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846167100756852736 |
|---|---|
| author | Yuqi Yan Danhui Gan Ping Zhang Haizhu Zou MinMin Li |
| author_facet | Yuqi Yan Danhui Gan Ping Zhang Haizhu Zou MinMin Li |
| author_sort | Yuqi Yan |
| collection | DOAJ |
| description | Background: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. Objective: The aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients. Methods: The clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD patients in Cohort 2 (n = 181) were externally verified. Results: We finally identified six independent risk factors for predicting NASH, including neutrophil percentage (NEU%), aspartate aminotransferase/alanine aminotransferase (AST/ALT), hematocrit (HCT), creatinine (CREA), uric acid (UA), and prealbumin (PA). The NASH-XGB6 model built using the XGBoost algorithm showed sufficient prediction accuracy, with ROC values of 0.95 (95 % CI, 0.91–0.98) and 0.90 (95 % CI, 0.88–0.93) in Cohort 1 and Cohort 2, respectively. Conclusions: NASH-XGB6 can serve as an effective tool for distinguishing NASH patients from NAFLD patients. |
| format | Article |
| id | doaj-art-353023a0a64747088938223cd1b8f37d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-353023a0a64747088938223cd1b8f37d2024-11-15T06:12:27ZengElsevierHeliyon2405-84402024-11-011021e38848A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver diseaseYuqi Yan0Danhui Gan1Ping Zhang2Haizhu Zou3MinMin Li4Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, ChinaDepartment of Clinical Pathology, The First Affiliated Hospital of Jinan University, Guangzhou, 510632, ChinaDepartment of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, ChinaDepartment of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, ChinaDepartment of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Corresponding author.Background: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. Objective: The aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients. Methods: The clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD patients in Cohort 2 (n = 181) were externally verified. Results: We finally identified six independent risk factors for predicting NASH, including neutrophil percentage (NEU%), aspartate aminotransferase/alanine aminotransferase (AST/ALT), hematocrit (HCT), creatinine (CREA), uric acid (UA), and prealbumin (PA). The NASH-XGB6 model built using the XGBoost algorithm showed sufficient prediction accuracy, with ROC values of 0.95 (95 % CI, 0.91–0.98) and 0.90 (95 % CI, 0.88–0.93) in Cohort 1 and Cohort 2, respectively. Conclusions: NASH-XGB6 can serve as an effective tool for distinguishing NASH patients from NAFLD patients.http://www.sciencedirect.com/science/article/pii/S2405844024148797NAFLDMachine learningNASHNon-invasive tests (NITs) |
| spellingShingle | Yuqi Yan Danhui Gan Ping Zhang Haizhu Zou MinMin Li A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease Heliyon NAFLD Machine learning NASH Non-invasive tests (NITs) |
| title | A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| title_full | A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| title_fullStr | A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| title_full_unstemmed | A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| title_short | A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| title_sort | machine learning based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease |
| topic | NAFLD Machine learning NASH Non-invasive tests (NITs) |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024148797 |
| work_keys_str_mv | AT yuqiyan amachinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT danhuigan amachinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT pingzhang amachinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT haizhuzou amachinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT minminli amachinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT yuqiyan machinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT danhuigan machinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT pingzhang machinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT haizhuzou machinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease AT minminli machinelearningbasedpredictivemodeldiscriminatesnonalcoholicsteatohepatitisfromnonalcoholicfattyliverdisease |