Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients
BackgroundSepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in pr...
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Frontiers Media S.A.
2025-01-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1496869/full |
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| author | Hongsheng Wu Biling Liao Tengfei Ji Keqiang Ma Yumei Luo Shengmin Zhang |
| author_facet | Hongsheng Wu Biling Liao Tengfei Ji Keqiang Ma Yumei Luo Shengmin Zhang |
| author_sort | Hongsheng Wu |
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| description | BackgroundSepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.ObjectiveTo develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.MethodsRetrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set.ResultsUnivariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung infection. Significant differences (p < 0.05) between the survival and non-survival groups were observed for these variables. Utilizing stepwise regression with the “backward” method, independent risk factors, including systolic pressure, lactic acid, NLR, RDW, IL-6, PT, and Tbil, were identified. These factors were then incorporated into a logistic regression model, chosen based on the minimum Akaike Information Criterion (AIC) value (98.65). Machine learning techniques were also applied, and the RF model, demonstrating the maximum Area Under the Curve (AUC) of 0.999, was selected. LASSO regression, employing the lambda.1SE criteria, identified systolic pressure, lactic acid, NEUT, RDW, IL6, INR, and Tbil as variables for constructing the RF model, validated through ten-fold cross-validation. For model validation and comparison with traditional logistic models, SOFA, and APACHE scoring.ConclusionBased on deep machine learning principles, the RF model demonstrates advantages over traditional logistic regression models in predicting adult sepsis prognosis. The RF model holds significant potential for clinical surveillance and interventions to enhance outcomes for sepsis patients. |
| format | Article |
| id | doaj-art-26f9fb4508eb43e3b85068b1e1bfe26c |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-26f9fb4508eb43e3b85068b1e1bfe26c2025-01-06T06:59:50ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.14968691496869Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patientsHongsheng WuBiling LiaoTengfei JiKeqiang MaYumei LuoShengmin ZhangBackgroundSepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.ObjectiveTo develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.MethodsRetrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set.ResultsUnivariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung infection. Significant differences (p < 0.05) between the survival and non-survival groups were observed for these variables. Utilizing stepwise regression with the “backward” method, independent risk factors, including systolic pressure, lactic acid, NLR, RDW, IL-6, PT, and Tbil, were identified. These factors were then incorporated into a logistic regression model, chosen based on the minimum Akaike Information Criterion (AIC) value (98.65). Machine learning techniques were also applied, and the RF model, demonstrating the maximum Area Under the Curve (AUC) of 0.999, was selected. LASSO regression, employing the lambda.1SE criteria, identified systolic pressure, lactic acid, NEUT, RDW, IL6, INR, and Tbil as variables for constructing the RF model, validated through ten-fold cross-validation. For model validation and comparison with traditional logistic models, SOFA, and APACHE scoring.ConclusionBased on deep machine learning principles, the RF model demonstrates advantages over traditional logistic regression models in predicting adult sepsis prognosis. The RF model holds significant potential for clinical surveillance and interventions to enhance outcomes for sepsis patients.https://www.frontiersin.org/articles/10.3389/fmed.2024.1496869/fullmachine learningrandom forestlogistic regressionadult sepsismortality |
| spellingShingle | Hongsheng Wu Biling Liao Tengfei Ji Keqiang Ma Yumei Luo Shengmin Zhang Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients Frontiers in Medicine machine learning random forest logistic regression adult sepsis mortality |
| title | Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| title_full | Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| title_fullStr | Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| title_full_unstemmed | Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| title_short | Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| title_sort | comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients |
| topic | machine learning random forest logistic regression adult sepsis mortality |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1496869/full |
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