Predicting Risk of Acute Appendicitis: A Comparison of Artificial Neural Network and Logistic Regression Models
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Leila Shahmoradi
,
Reza Safdari
,
Mir Mikail Mirhosseini
,
Goli Arji
,
Behrooz Jannat
,
Moloud Abdar
Abstract
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.
Ting H-W, Wu J-T, Chan C-L, Lin S-L, Chen M-H. Decision model for acute appendicitis treatment with decision tree technology—a modification of the alvarado scoring system. Journal of the chinese medical association. 2010;73(8):401-6.
Akbulut S, Ulku A, Senol A, Tas M, Yagmur Y. Left-sided appendicitis: review of 95 published cases and a case report. World Journal of Gastroenterology: WJG. 2010;16(44):5598.
Ferris M, Quan S, Kaplan BS, Molodecky N, Ball CG, Chernoff GW, et al. The Global Incidence of Appendicitis: A Systematic Review of Population-based Studies. Annals of Surgery. 2017;266(2):237-41.
Zorman M, Eich HP, Kokol P, Ohmann C. Comparison of three databases with a decision tree approach in the medical field of acute appendicitis. Medinfo MEDINFO. 2001;10(Pt 2):1414-8.
Memon ZA, Irfan S, Fatima K, Iqbal MS, Sami W. Acute appendicitis: diagnostic accuracy of Alvarado scoring system. Asian Journal of surgery. 2013;36(4):144-9.
Hsieh CH, Lu RH, Lee NH, Chiu WT, Hsu MH, Li YC. Novel solutions for an old disease: Diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery. 2011;149(1):87-93.
Prabhudesai SG, Gould S, Rekhraj S, Tekkis PP, Glazer G, Ziprin P. Artificial neural networks: Useful aid in diagnosing acute appendicitis. World Journal of Surgery. 2008;32(2):305-9.
Liu JL, Wyatt JC, Deeks JJ, Clamp S, Keen J, Verde P, et al. Systematic reviews of clinical decision tools for acute abdominal pain. Health technology assessment (Winchester, England). 2006;10(47):1-167, iii-iv.
Kollár D, McCartan D, Bourke M, Cross K, Dowdall J. Predicting acute appendicitis? A comparison of the Alvarado score, the Appendicitis Inflammatory Response Score and clinical assessment. World journal of surgery. 2015;39(1):104-9.
Di Saverio S, Tugnoli G. The Challenge of Clinical Diagnosis of Appendicitis and Nonoperative Management of Patients With Right Lower Abdominal Pain. Annals of surgery. 2016;263(2):e22-e3.
Alter SM, Walsh B, Lenehan PJ, Shih RD. Ultrasound for Diagnosis of Appendicitis in a Community Hospital Emergency Department has a High Rate of Nondiagnostic Studies. The Journal of Emergency Medicine. 2017.
Cohen B, Bowling J, Midulla P, Shlasko E, Lester N, Rosenberg H, et al. The non-diagnostic ultrasound in appendicitis: is a non-visualized appendix the same as a negative study? Journal of pediatric surgery. 2015;50(6):923-7.
Siegel Y, Kuker R, Banks J, Danton G. CT pulmonary angiogram quality comparison between early and later pregnancy. Emergency Radiology. 2017:1-6.
Aggenbach L, Zeeman G, Cantineau A, Gordijn S, Hofker H. Impact of appendicitis during pregnancy: no delay in accurate diagnosis and treatment. International Journal of Surgery. 2015;15:84-9.
Sosner E, Patlas MN, Chernyak V, Dachman AH, Katz DS. Missed Acute Appendicitis on Multidetector Computed Tomography and Magnetic Resonance Imaging: Legal Ramifications, Challenges, and Avoidance Strategies. Current Problems in Diagnostic Radiology. 2017.
Yilmaz O, Bozdana AT, Okka MA. An intelligent and automated system for electrical discharge drilling of aerospace alloys: Inconel 718 and Ti-6Al-4V. The International Journal of Advanced Manufacturing Technology. 2014;74(9-12):1323-36.
Heydari M, Teimouri M, Heshmati Z, Alavinia SM. Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. International Journal of Diabetes in Developing Countries. 2016;36(2):167-73.
Tseng C-J, Lu C-J, Chang C-C, Chen G-D, Cheewakriangkrai C. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artificial Intelligence in Medicine. 2017.
Dey S, Mohanta PK, Baruah AK, Kharga B, Bhutia KL, Singh VK. Alvarado scoring in acute appendicitis—a clinicopathological correlation. Indian journal of surgery. 2010;72(4):290-3.
Nanjundaiah N, Mohammed A, Shanbhag V, Ashfaque K, Priya S. A comparative study of RIPASA score and ALVARADO score in the diagnosis of acute appendicitis. Journal of clinical and diagnostic research: JCDR. 2014;8(11):NC03.
Kang F, Li J, Xu Q. System reliability analysis of slopes using multilayer perceptron and radial basis function networks. International Journal for Numerical and Analytical Methods in Geomechanics. 2017.
Nilashi M, Ahmadi H, Shahmoradi L, Mardani A, Ibrahim O, Yadegaridehkordi E. Knowledge Discovery and Diseases Prediction: A Comparative Study of Machine Learning Techniques. Journal of Soft Computing and Decision Support Systems. 2017;4(5):8-16.
Hossain MS, Ong ZC, Ismail Z, Khoo SY. A comparative study of vibrational response based impact force localization and quantification using radial basis function network and multilayer perceptron. Expert Systems with Applications. 2017;85:87-98.
Nilashi M, Ibrahim Ob, Ahmadi H, Shahmoradi L. An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering. 2017;106:212-23.
Gholami A, Bonakdari H, Zaji AH, Michelson DG, Akhtari AA. Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90 bend. Applied Soft Computing. 2016;48:563-83.
Araujo P, Astray G, Ferrerio-Lage J, Mejuto J, Rodriguez-Suarez J, Soto B. Multilayer perceptron neural network for flow prediction. Journal of Environmental Monitoring. 2011;13(1):35-41.
Delen D, Oztekin A, Kong ZJ. A machine learning-based approach to prognostic analysis of thoracic transplantations. Artificial Intelligence in Medicine. 2010;49(1):33-42.
Huang M-L, Chen H-Y, Huang W-C, Tsai Y-Y. Linear discriminant analysis and artificial neural network for glaucoma diagnosis using scanning laser polarimetry–variable cornea compensation measurements in Taiwan Chinese population. Graefe's Archive for Clinical and Experimental Ophthalmology. 2010;248(3):435-41.
Shen Y. Adaptive online state-of-charge determination based on neuro-controller and neural network. Energy Conversion and Management. 2010;51(5):1093-8.
Son CS, Jang BK, Seo ST, Kim MS, Kim YN. A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis. BMC Medical Informatics and Decision Making. 2012;12(1).
Park SY, Kim SM. Acute appendicitis diagnosis using artificial neural networks. Technology and Health Care. 2015;23(s2):S559-S65.
Yoldaş Ö, Tez M, Karaca T. Artificial neural networks in the diagnosis of acute appendicitis. The American Journal of Emergency Medicine. 2012;30(7):1245-7.
Pesonen E, Eskelinen M, Juhola M. Comparison of different neural network algorithms in the diagnosis of acute appendicitis. International Journal of Bio-Medical Computing. 1996;40(3):227-33.
Sakai S, Kobayashi K, Nakamura J, Toyabe S, Akazawa K. Accuracy in the diagnostic prediction of acute appendicitis based on the Bayesian network model. Methods of Information in Medicine. 2007;46(6):723-6.
Park SY, Lee S, Jeong JH, Kim SM. Application of artificial neural networks for diagnosing acute appendicitis. 2014. p. 445-50.
Yu CW, Juan LI, Wu MH, Shen CJ, Wu JY, Lee CC. Systematic review and meta‐analysis of the diagnostic accuracy of procalcitonin, C‐reactive protein and white blood cell count for suspected acute appendicitis. British Journal of Surgery. 2013;100(3):322-9.
Emmanuel A, Murchan P, Wilson I, Balfe P. The value of hyperbilirubinaemia in the diagnosis of acute appendicitis. The Annals of The Royal College of Surgeons of England. 2011;93(3):213-7.
| Files | ||
| Issue | Vol 56, No 12 (2018) | |
| Section | Original Article(s) | |
| Keywords | ||
| Acute appendicitis Neural network Multi-layer perceptron Radial-based function Logistic regression | ||
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