Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data
<p>Abstract</p> <p>Background</p> <p>In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages...
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| Format: | Article |
| Language: | English |
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BMC
2005-02-01
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| Series: | BMC Medical Informatics and Decision Making |
| Online Access: | http://www.biomedcentral.com/1472-6947/5/3 |
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| author | Ghodsi Mohammad Ardebili Hassan Mohammad Kazem Eftekhar Behzad Ketabchi Ebrahim |
| author_facet | Ghodsi Mohammad Ardebili Hassan Mohammad Kazem Eftekhar Behzad Ketabchi Ebrahim |
| author_sort | Ghodsi Mohammad |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings.</p> <p>Methods</p> <p>1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests.</p> <p>Results</p> <p>ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model.</p> <p>Conclusions</p> <p>ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population.</p> |
| first_indexed | 2024-12-19T00:11:35Z |
| format | Article |
| id | doaj.art-68e88f8650144084a3bde67905a203fa |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-19T00:11:35Z |
| publishDate | 2005-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-68e88f8650144084a3bde67905a203fa2022-12-21T20:46:01ZengBMCBMC Medical Informatics and Decision Making1472-69472005-02-0151310.1186/1472-6947-5-3Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical dataGhodsi MohammadArdebili HassanMohammad KazemEftekhar BehzadKetabchi Ebrahim<p>Abstract</p> <p>Background</p> <p>In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings.</p> <p>Methods</p> <p>1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests.</p> <p>Results</p> <p>ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model.</p> <p>Conclusions</p> <p>ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population.</p>http://www.biomedcentral.com/1472-6947/5/3 |
| spellingShingle | Ghodsi Mohammad Ardebili Hassan Mohammad Kazem Eftekhar Behzad Ketabchi Ebrahim Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data BMC Medical Informatics and Decision Making |
| title | Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| title_full | Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| title_fullStr | Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| title_full_unstemmed | Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| title_short | Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| title_sort | comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data |
| url | http://www.biomedcentral.com/1472-6947/5/3 |
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