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Prediction of disease severity is highly essential for understanding the progression of disease and initiating an early diagnosis, which is priceless in treatment planning. A Modified Cascade Neural Network (ModCNN) is proposed for stratification of the patients who may need Endoscopic Retrograde Cholangiopancreatography (ERCP). In this study, gallstone disease (GSD) whose prevalence is increasing in India is considered. A retrospective analysis of 100 patients was conducted and their case history was recorded along with the routine investigations. Using ModCNN, the associated risk factors were extracted for the prediction of disease progression toward severe complication. The proposed model outperformed showing better accuracy with an area under receiver operating characteristic curve (area under ROC curve) of 0.9793, 0.9643, 0.9869, and 0.9768 for choledocholithiasis, pancreatitis, cholecystitis, and cholangitis, respectively, when compared with Artificial Neural Network (ANN) showing an accuracy of 0.884. Hence, the proposed technique can be used to conduct a nonlinear statistical analysis for the better prediction of disease progression and assist in better treatment planning, avoiding future complications.
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Huff S. M., Rocha R. A., Bray B. E., Warner H. R. and Haug P. J. “An event model of medical information representation”, Journal of the American Medical Informatics Association, Vol. 2 no. 2, pp. 116–134, Mar. 1995.
Garg P. K., editor, “Chronic Pancreatitis-ECAB”. Elsevier Health Sciences, 2013 Jun. 17.
Khuroo M. S., Mahajan R., Zargar S. A., Javid G. and Munshi S. “Prevalence of peptic ulcer in India: an endoscopic and epidemiological study in urban Kashmir”. Gut., Vol. 30, no. 7, pp. 930–934, Jul. 1989.
Kapoor V. K. “Cholecystectomy in patients with asymptomatic gallstones to prevent gall bladder cancer–the case against”, Indian Society of Gastroenterology, 2006.
Jovanovic, Predrag, Nermin N. Salkic, and Enver Zerem. “Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis”, Gastrointestinal endoscopy, Vol. 80, no. 2, pp. 260–268, Aug. 2014.
Tourassi G. D., Floyd C. E, Sostman H. D. and Coleman R. E. “Acute pulmonary embolism: artificial neural network approach for diagnosis”, Radiology, Vol. 189, no. 2, pp. 555–558, Nov. 1993.
Chan H. P., Sahiner B., Petrick N., Helvie M. A., Lam K. L., Adler D. D. and Goodsitt M. M. “Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network”, Phys. in Med. and Biol., Vol. 42, no. 3, pp. 549–567, Mar. 1997.
Baker J. A., Kornguth P. J., Lo J. Y., Williford M. E. and Floyd Jr. C. E. “Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon”. Radiology, Vol. 196, no. 3, pp. 817–822, Sep. 1995.
Fujita H., Katafuchi T., Uehara T. and Nishimura T. “Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull’s-eye images”, J. Nucl. Med. Vol. 33, pp. 272–276, 1992.
Ashizawa K., Ishida T., MacMahon H., Vyborny C. J., Katsuragawa S. and Doi K. “Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease”, Acad. Radiol., Vol. 6, no. 1, pp. 2–9, Jan 1999.
Keogan M. T., Lo J. Y., Freed K. S., Raptopoulos V., Blake S., Kamel I. R., Weisinger K., Rosen M. P, and Nelson R. C. “Outcome analysis of patients with acute pancreatitis by using an artificial neural network”. Academic radiology, Vol. 9, no. 4, pp. 410–419, Apr. 2002.
Fahlman Scott E., and Christian Lebiere, “The cascade-correlation learning architecture”. Vol. 2, 1989.
Glasgow R. E., Cho M., Hutter M. M. and Mulvihill S. J. “The spectrum and cost of complicated gallstone disease in California”. Archives of Surgery, Vol. 135, no. 9, pp. 1021–1025, Sep. 2000.
Mc. Culloch W. S. and Pitts W. “A logical calculus of the ideas immanent in nervous activity”. The bulletin of mathematical biophysics, Vol. 5, no. 4, pp. 115–33, Dec 1943.
- Prediction of Gallstone Disease Progression Using Modified Cascade Neural Network
M. V. Manoj Kumar
- Springer Singapore
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