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\title{Prediction Analysis of Esophageal Variceal Degrees using Data Mining: Is Validated in Clinical Medicine?}
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             \author[1]{Abd  Mohammad Aly Abd  Elrazek}

             \author[2]{Hamdy  Mahfouz}

             \affil[1]{  Al-Azhar university}

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\date{\small \em Received: 14 December 2012 Accepted: 4 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


The objective of this study is to assess the feasibility of a data mining association analysis technique in early prediction of esophageal varices in cirrhotic patients and prediction of risky groups candidates for urgent interventional procedure. A manuscript titled "Detection of Risky Esophageal varices using 2D U/S: when to perform Endoscopy", published in The American Journal of The Medical Science on 21Th of December 2012, to our knowledge it was the first prospective study to assess the degree of esophageal varices by 2D ultrasound using the data mining statistical computed analysis in 673 patients. A descriptive model was generated using a decision tree algorithm (Rapid Miner, version 4.6, Berlin, Germany), the over all accuracy was 95%. Following another 59 patients using statistical analysis to determine the association between esophageal variceal degrees detected by Ultrasound in comparable to Upper Endoscopy, was done.Categorical data were compared using the x 2 test, where as continuous variables were compared using Student's t test.The comparative results accuracy of both two studies was 97.9%.

\end{abstract}


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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
lthough the rate of mortality from GI bleeding episodes has decreased with improved endoscopic and radiologic techniques together with new pharmacologic therapies, the 13\% to 20\% mortality implies the clinical importance, whatever most of mortalities due vaiceal rupture or leakage \hyperref[b0]{[1]}. Concern has been raised about the prediction of Esophageal varices for those expected to develop gastrointestinal bleeding in the future, analyzing data by Data mining programs discovered the significant important leading mortality factors; risky Esophageal varices \hyperref[b1]{[2]}. Thus the major challenge of biomedical data mining over the next 5-10 years is to make these systems useful to biomedical researchers \hyperref[b2]{[3]}. This paper discuss the utility usefulness of data mining as a predictor statistical tool used to predict esophageal variceal degrees in cirrhotic patients, through measuring the intra-abdominal esophageal wall thickness, by non invasive ultrasound technique. 
\section[{Authors}]{Authors}\par
: Tropical Medicine Department, (Medical Informatics) Al-Azhar Faculty of Medicine-Egypt. E-mail : ahmadrazek@gmail.com II.  \hyperref[b3]{[4]}. 
\section[{Clinical View}]{Clinical View}\par
Helicobacter pylori infection, Non Steroidal antiinflammatory drugs (NSAIDs), physiologic stress, and excess gastric acid are major risk factors for bleeding peptic ulcers. Reduction or elimination of these risk factors reduces ulcer recurrence and rebleeding rates, whatever Esophagogastric varices develop as a consequence of portal hypertension Less common causes of upper gastrointestinal bleeding include Hemobilia, Hemosuccus pancreaticus, Aortoenteric fistula and Cameron lesions. \hyperref[b4]{[5,}\hyperref[b5]{6]}.  
\section[{Prediction Analysis of Esophageal Variceal}]{Prediction Analysis of Esophageal Variceal}\par
The comparative results accuracy of both two studies Esophageal varices are swollen blood vessels in the esophagus, which is the tube that connects the mouth to the stomach. Esophageal varices often happen in people with serious liver disease, called "cirrhosis." As a result of portal hypertension [Figure1], Cirrhosis is a medical term, usually used to describe a diseased liver that has been severely scarred injury. There are numerous causes of liver cirrhosis, the two most common causes of cirrhosis worldwide are alcoholic liver disease and viral hepatitis C, which together accounted for approximately one-half of patients on the liver transplantation wait list worldwide whatever portal hypertension can also be present in the absence of cirrhosis, a condition referred to as "noncirrhotic portal hypertension". The causes of noncirrhotic portal hypertension can be divided into prehepatic, intrahepatic and post hepatic (presinusoidal, sinusoidal and post sinusoidal causes). Early endoscopy (within 24 hours) is recommended for most patients with acute UGI bleeding, though whether early endoscopy affects outcomes and resource utilization is unsettled \hyperref[b6]{[7]}. 
\section[{d) Non-Invasive Diagnoses}]{d) Non-Invasive Diagnoses}\par
The identification of Cirrhotic patients with esophageal varices using non-invasive means has been attempted using different clinical, laboratory and radiological approaches, unfortunately many of these approaches still controversial ,recently non-invasive 2 D U/S can detect esophageal variceal degrees in cirrhotic patients with high accuracy, through measuring the intra-abdominal wall thickness of the esophagus, there were proportional relations between esophageal wall thicknesses detected by ultrasound and the esophageal variceal degrees diagnosed by Upper Endoscopy. 
\section[{III.}]{III.} 
\section[{Analysis by Data Mining}]{Analysis by Data Mining}\par
The basic classification is based on supervised algorithm, whatever algorithms are applicable for the input data, the accuracy of each algorithm could be changed according to data nature. In clinical medicine, there are different laboratory, clinical and radiological factors determine the progress of each disease. Identification of important and risky factors is of great importance to predict out come in each disease stage. Analyzing of our data by data mining computed program shed light on the significant important factors for each disease condition. Thus the major challenge of biomedical data mining is to make these systems useful to biomedical researchers. The decision tree analysis was performed using Intelligent Miner software (Rapid Miner, Berlin, ver.4.6, Germany), which can automatically search a data set to find the optimal classification variables leading to the building of a decision tree algorithm . Briefly, all items derived from the patients were evaluated to determine which variables and cutoff points might produce the most significant division into two subgroups; group with risky esophageal varices when esophageal wall thickness >6.5 mm and not risky group when esophageal wall thickness IV. 
\section[{Validation Accuracy}]{Validation Accuracy}\par
In our previous study the algorithm was selected by evaluating each supervised machine learning algorithms by using supervised learning assessment (10-folds-cross validation) on the training set, we chose the best test applicable to our clinical data, accordingly we used Naïve-base test, and the overall accuracy we obtained was 95\%.\par
We followed 59 patients presented with portal hypertension as a result of End stage-liver cirrhosis; the thicknesses of esophageal walls were measured using 2D U/S. All patients underwent diagnostic Esophagogastrodudonoscopy to estimate corresponding degrees of varices. According to the decision tree algorithm we obtained from the previous study, the esophageal wall thickness > 4.2 mm with inner wall irregularities, should be Esophageal varices with variable degrees, esophageal wall thickness <4.2mm measured by U/S without inner wall irregularities, V. 
\section[{Results}]{Results}\par
Categorical data were compared using the 2 test, where as continuous variables were compared using Student's t test.\par
The overall comparative validation accuracy was 97.9 \%. Table  {\ref [}  
\section[{Conclusion}]{Conclusion}\par
Based on the available evidence, prediction identification of esophageal variceal degrees in cirrhotic patients presented with manifestations of portal hypertension using non-invasive 2D ultrasound could be a very helpful tool saving time and money. Data mining shed light on the most significant predictors-related esophageal wall thicknesses in 673 patients with very high accuracy; identification high risky groups needed urgent interventional Endoscopy; less complication, better out come and decrease mortality.\par
Data mining would be the coming statistical evolution in clinical medical data statistical analysis with reasonable limitation. 
\section[{VII.}]{VII.} 
\section[{Limitation of the Study}]{Limitation of the Study}\par
Given the small sample size of 59 patients (8.7\%) in comparable to 673 reported in the pervious study, the validation accuracy might be changed if we apply more patients in the future, whatever our clinical experience played a major role in assessing the information mentioned above, in our point of view our results should be confirmed with more evidence-based criteria using independent laboratory and clinical factors all together.  \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}©}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.5522292993630573\textwidth}P{0.05414012738853503\textwidth}P{0.05955414012738853\textwidth}P{0.18407643312101912\textwidth}}
Esophageal wall Thickness\tabcellsep Cases\tabcellsep Result\tabcellsep Comparative\\
< 4.2 ; No varices\tabcellsep 6\tabcellsep 6\tabcellsep 100\%\\
>4.2 mm*; Varices\tabcellsep 53\tabcellsep 53\tabcellsep 100\%\\
>6.5 mm; Risky varices\tabcellsep 48\tabcellsep 45\tabcellsep 93.75\%\\
Over All Accuracy\tabcellsep \tabcellsep \tabcellsep 97.9\%\\
VI.\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Yang and Wu ()]{b20}\label{b20} 	 		‘10 Challenging Problems in Data Mining Research’.  		 			Q Yang 		,  		 			Xindong Wu 		.  	 	 		\textit{International Journal of Information Technology \& Decision Making}  		2006. 5  (4)  p. .  	 
\bibitem[Lanas et al. ()]{b3}\label{b3} 	 		‘A nationwide study of mortality associated with hospital admission due to severe gastrointestinal events and those associated with nonsteroidal antiinflammatory drug use’.  		 			A Lanas 		,  		 			M A Perez-Aisa 		,  		 			F Feu 		.  	 	 		\textit{Am J Gastroenterol}  		2005. 100 p. 1685.  	 
\bibitem[Wilcox et al. ()]{b5}\label{b5} 	 		‘A prospective characterization of upper gastrointestinal hemorrhage presenting with hematochezia’.  		 			C M Wilcox 		,  		 			L N Alexander 		,  		 			G Cotsonis 		.  	 	 		\textit{Am J Gastroenterol}  		1997. 92 p. 231.  	 
\bibitem[Cohen and Hersh ()]{b2}\label{b2} 	 		‘A survey of current work in biomedical text mining’.  		 			A M Cohen 		,  		 			W R Hersh 		.  	 	 		\textit{Briefings in Bioinformatics}  		2005. 6 p. .  	 
\bibitem[Nakayama et al. (2012)]{b10}\label{b10} 	 		‘Algorithm to determine the outcome of patients with acute liver failure: a data-mining analysis using decision trees’.  		 			Nobuaki Nakayama 		,  		 			Makoto Oketani 		,  		 			Yoshihiro Kawamura 		,  		 			Mie Inao 		,  		 			Sumiko Nagoshi 		,  		 			Kenji Fujiwara 		,  		 			Hirohito Tsubouchi 		,  		 			Satoshi Mochida 		.  	 	 		\textit{J Gastroenterol}  		2012 June. 47  (6)  p. .  	 
\bibitem[Zuckerman et al. ()]{b16}\label{b16} 	 		‘An objective measure of stool color for differentiating upper from lower gastrointestinal bleeding’.  		 			G R Zuckerman 		,  		 			D R Trellis 		,  		 			T M Sherman 		,  		 			R E Clouse 		.  	 	 		\textit{Dig Dis Sci}  		1995. 40 p. 1614.  	 
\bibitem[Ghany et al. ()]{b24}\label{b24} 	 		‘An update on treatment of genotype 1 chronic hepatitis C virus infection: 2011 practice guideline by the’.  		 			M G Ghany 		,  		 			D R Nelson 		,  		 			D B Strader 		.  	 	 		\textit{Hepatology}  		2011. American Association for the Study of Liver Diseases. 54 p. 1433.  	 
\bibitem[Berardis et al. ()]{b4}\label{b4} 	 		‘Association of aspirin use with major bleeding in patients with and without diabetes’.  		 			De Berardis 		,  		 			G Lucisano 		,  		 			G 		,  		 			D 'ettorre 		,  		 			A 		.  	 	 		\textit{JAMA}  		2012. 307 p. 2286.  	 
\bibitem[Groszmann et al. ()]{b14}\label{b14} 	 		‘Betablockers to prevent gastroesophageal varices in patients with cirrhosis’.  		 			R J Groszmann 		,  		 			G Garcia-Tsao 		,  		 			J Bosch 		.  	 	 		\textit{N Engl J Med}  		2005. 353 p. 2254.  	 
\bibitem[Basara and Yuan ()]{b9}\label{b9} 	 		‘Community health assessment using self-organizing maps and geographic information systems’.  		 			H G Basara 		,  		 			M Yuan 		.  		 \xref{http://dx.doi.org/10.1186/1476-072X-7-67}{10.1186/1476-072X-7-67}.  	 	 		\textit{Int. J Health Geogr}  		2008. 30  (7)  p. 67.  	 
\bibitem[El Razek (2012)]{b1}\label{b1} 	 		‘Detection of Risky Esophageal varices by 2D U/S: when to perform Endoscopy’.  		 			Abd El Razek 		.  	 	 		\textit{Am J Med Sci}  		2012 Dec 21.  	 	 (Epub ahead of print) 
\bibitem[Jensen and Machicado ()]{b15}\label{b15} 	 		‘Diagnosis and treatment of severe hematochezia. The role of urgent colonoscopy after purge’.  		 			D M Jensen 		,  		 			G A Machicado 		.  	 	 		\textit{Gastroenterology}  		1988. 95 p. 1569.  	 
\bibitem[Udell et al. ()]{b23}\label{b23} 	 		‘Does this patient with liver disease have cirrhosis?’.  		 			J A Udell 		,  		 			C S Wang 		,  		 			J Tinmouth 		.  	 	 		\textit{JAMA}  		2012. 307 p. 832.  	 
\bibitem[Endoscopic management of Dieulafoy's lesion using Isoamyl-2-cyanoacrylate World Journal of Gastrointestinal Endoscopy (2013)]{b27}\label{b27} 	 		‘Endoscopic management of Dieulafoy's lesion using Isoamyl-2-cyanoacrylate’.  		 23951399.  	 	 		\textit{World Journal of Gastrointestinal Endoscopy}  		2013 Aug 16. 5  (8)  p. .  	 
\bibitem[Lu et al. (2013)]{b21}\label{b21} 	 		‘From Data towards Knowledge: Revealing the Architecture of Signaling Systems by Unifying Knowledge Mining and Data Mining of Systematic Perturbation Data’.  		 			S Lu 		,  		 			Jin B Cowart 		,  		 			L A Lu 		,  		 			X 		.  	 	 		\textit{Plos One}  		2013 Apr 23. 8  (4)  p. e61134.  	 
\bibitem[Parsonnet et al. ()]{b17}\label{b17} 	 		‘Helicobacter pylori infection and gastric lymphoma’.  		 			J Parsonnet 		,  		 			S Hansen 		,  		 			L Rodriguez 		.  	 	 		\textit{N Engl J Med}  		1994. 330 p. 1267.  	 
\bibitem[Shibata et al. ()]{b19}\label{b19} 	 		‘Helicobacter pylori infection in patients with gastric carcinoma in biopsy and surgical resection specimens’.  		 			T Shibata 		,  		 			I Imoto 		,  		 			Y Ohuchi 		.  	 	 		\textit{Cancer}  		1996. 77 p. 1044.  	 
\bibitem[De Souza et al. ()]{b12}\label{b12} 	 		‘Patients whose first episode of bleeding occurs while taking a ?-blocker have high long-term risks of rebleeding and death’.  		 			A R De Souza 		,  		 			La Mura 		,  		 			V Reverter 		,  		 			E 		.  	 	 		\textit{Clin Gastroenterol Hepatol}  		2012. 10 p. 670.  	 
\bibitem[Bellazzi and Zupan ()]{b22}\label{b22} 	 		‘Predictive data mining in clinical medicine: current issues and guidelines’.  		 			R Bellazzi 		,  		 			B Zupan 		.  		 \xref{http://dx.doi.org/10.1016/j.ijmedinf.2006.11.006}{10.1016/j.ijmedinf.2006.11.006}.  	 	 		\textit{Int J Med Inform}  		2008. 77 p. .  	 
\bibitem[Cheung et al. ()]{b26}\label{b26} 	 		‘Prevalence and mechanisms of malnutrition in patients with advanced liver disease, and nutrition management strategies’.  		 			K Cheung 		,  		 			S S Lee 		,  		 			M Raman 		.  	 	 		\textit{Clin Gastroenterol Hepatol}  		2012. 10 p. 117.  	 
\bibitem[Talbi and Charef ()]{b8}\label{b8} 	 		‘PVC discrimination using the QRS power spectrum and self-organizing maps’.  		 			M L Talbi 		,  		 			A Charef 		.  		 \xref{http://dx.doi.org/10.1016/j.cmpb.2008.12.009}{10.1016/j.cmpb.2008.12.009}.  	 	 		\textit{Comput Methods Programs Biomed}  		2009. 94 p. .  	 
\bibitem[Pajares ()]{b18}\label{b18} 	 		‘pylori infection: its role in chronic gastritis, carcinoma and peptic ulcer’.  		 			Jm H Pajares 		.  	 	 		\textit{Hepatogastroenterology}  		1995. 42 p. 827.  	 
\bibitem[Crooks et al. ()]{b0}\label{b0} 	 		‘Reductions in 28-day mortality following hospital admission for upper gastrointestinal hemorrhage’.  		 			C Crooks 		,  		 			T Card 		,  		 			J West 		.  	 	 		\textit{Gastroenterology}  		2011. 141 p. 62.  	 
\bibitem[Kohonen ()]{b7}\label{b7} 	 		\textit{Self-organizing maps},  		 			T Kohonen 		.  		2001. Berlin: Springer.  	 
\bibitem[Balderas et al. ()]{b6}\label{b6} 	 		‘The hematocrit level in upper gastrointestinal hemorrhage: safety of endoscopy and outcomes’.  		 			V Balderas 		,  		 			R Bhore 		,  		 			L F Lara 		.  	 	 		\textit{Am J Med}  		2011. 124 p. 970.  	 
\bibitem[Balderas et al. ()]{b11}\label{b11} 	 		‘The hematocrit level in upper gastrointestinal hemorrhage: safety of endoscopy and outcomes’.  		 			V Balderas 		,  		 			R Bhore 		,  		 			L F Lara 		.  	 	 		\textit{Am J Med}  		2011. 124 p. 970.  	 
\bibitem[Asrani et al. ()]{b13}\label{b13} 	 		‘Underestimation of liver-related mortality in the United States’.  		 			S K Asrani 		,  		 			J J Larson 		,  		 			B Yawn 		.  	 	 		\textit{Gastroenterology}  		2013. 145 p. 375.  	 
\bibitem[US Burden of Disease Collaborators. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors JAMA ()]{b25}\label{b25} 	 		‘US Burden of Disease Collaborators. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors’.  	 	 		\textit{JAMA}  		2013. 310 p. 591.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
