Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models
Acute appendicitis is considered as one of the most prevalent diseases needing urgent action. Diagnosis of appendicitis is often complicated, and more precision in diagnosis is essential. The aim of this paper was to construct a model to predict acute appendicitis based on pathology reports. The ana...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2019-01-01
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| Series: | Acta Medica Iranica |
| Subjects: | |
| Online Access: | https://acta.tums.ac.ir/index.php/acta/article/view/7363 |
| _version_ | 1811315403222155264 |
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| author | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Goli Arji Behrooz Jannat Moloud Abdar |
| author_facet | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Goli Arji Behrooz Jannat Moloud Abdar |
| author_sort | Leila Shahmoradi |
| collection | DOAJ |
| description | Acute appendicitis is considered as one of the most prevalent diseases needing urgent action. Diagnosis of appendicitis is often complicated, and more precision in diagnosis is essential. The aim of this paper was to construct a model to predict acute appendicitis based on pathology reports. The analysis included 181 patients with an early diagnosis of acute appendicitis who had admitted to Shahid Modarres hospital. Two well-known neural network models (Radial Basis Function Network (RBFNs) and Multi-Layer Perceptron (MLP)) and logistic regression model were developed based on 16 attributes related to acute appendicitis diagnosis respectively. Statistical indicators were applied to evaluate the value of the prediction in three models. The predicted sensitivity, specificity, positive predicted value, negative predictive values, and accuracy by using MLP for acute appendicitis were 80%, 97.5%, 92.3%, 93%, and 92.9%, respectively. Maine variables for correct diagnosis of acute appendicitis were leukocytosis, sex and tenderness, and right iliac fossa pain. According to the findings, the MLP model is more likely to predict acute appendicitis than RBFN and logistic regression. Accurate diagnosis of acute appendicitis is considered an essential factor for decreasing mortality rate. MLP based neural network algorithm revealed more sensitivity, specificity, and accuracy in timely diagnosis of acute appendicitis. |
| first_indexed | 2024-04-13T11:30:49Z |
| format | Article |
| id | doaj.art-64a51e5adcb54bbcb991234fa15f32a7 |
| institution | Directory Open Access Journal |
| issn | 0044-6025 1735-9694 |
| language | English |
| last_indexed | 2024-04-13T11:30:49Z |
| publishDate | 2019-01-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Acta Medica Iranica |
| spelling | doaj.art-64a51e5adcb54bbcb991234fa15f32a72022-12-22T02:48:35ZengTehran University of Medical SciencesActa Medica Iranica0044-60251735-96942019-01-0156127363Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression ModelsLeila Shahmoradi0Reza Safdari1Mir Mikail Mirhosseini2Goli Arji3Behrooz Jannat4Moloud Abdar5Health Information Management Department, School of Allied-Medical Sciences, Tehran University of Medical Sciences, Tehran, IranHealth Information Management Department, School of Allied-Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.Health Information Management Department, School of Allied-Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.Health Information Management Department, School of Allied-Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.Halal Research Center of Iran, Food and Drug Administration of the Islamic Republic of Iran, Tehran, Iran.Département d'Informatique, Université du Québec à Montréal, Montréal, Québec, Canada.Acute appendicitis is considered as one of the most prevalent diseases needing urgent action. Diagnosis of appendicitis is often complicated, and more precision in diagnosis is essential. The aim of this paper was to construct a model to predict acute appendicitis based on pathology reports. The analysis included 181 patients with an early diagnosis of acute appendicitis who had admitted to Shahid Modarres hospital. Two well-known neural network models (Radial Basis Function Network (RBFNs) and Multi-Layer Perceptron (MLP)) and logistic regression model were developed based on 16 attributes related to acute appendicitis diagnosis respectively. Statistical indicators were applied to evaluate the value of the prediction in three models. The predicted sensitivity, specificity, positive predicted value, negative predictive values, and accuracy by using MLP for acute appendicitis were 80%, 97.5%, 92.3%, 93%, and 92.9%, respectively. Maine variables for correct diagnosis of acute appendicitis were leukocytosis, sex and tenderness, and right iliac fossa pain. According to the findings, the MLP model is more likely to predict acute appendicitis than RBFN and logistic regression. Accurate diagnosis of acute appendicitis is considered an essential factor for decreasing mortality rate. MLP based neural network algorithm revealed more sensitivity, specificity, and accuracy in timely diagnosis of acute appendicitis.https://acta.tums.ac.ir/index.php/acta/article/view/7363Acute appendicitisNeural networkMulti-layer perceptronRadial-based functionLogistic regression |
| spellingShingle | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Goli Arji Behrooz Jannat Moloud Abdar Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models Acta Medica Iranica Acute appendicitis Neural network Multi-layer perceptron Radial-based function Logistic regression |
| title | Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models |
| title_full | Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models |
| title_fullStr | Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models |
| title_full_unstemmed | Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models |
| title_short | Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models |
| title_sort | predicting risk of acute appendicitis a comparison of artificial neural network and logistic regression models |
| topic | Acute appendicitis Neural network Multi-layer perceptron Radial-based function Logistic regression |
| url | https://acta.tums.ac.ir/index.php/acta/article/view/7363 |
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