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Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Na&#239ve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

The outcomes of surgical repair for diffuse pulmonary artery stenoses due to arterial tortuosity syndrome are unknown. We report a 1-year-old girl with tortuosity syndrome who presented with suprasystemic right ventricular pressure due to distortion and stenoses of the pulmonary artery branches. We describe the operative findings of this unique pathology and the surgical techniques used to completely reconstruct the pulmonary arterial tree, with normalization of the right ventricular pressure.

Arterial Tortuosity Syndrome (ATS) is an autosomal recessive connective tissue disease that can present with complex pulmonary arterial stenosis, causing right ventricular (RV) hypertension and dysfunction. In this study, we review our experience and early outcomes with single stage surgical repair.From 2008 to 2010, 7 patients with ATS and severe bilateral pulmonary artery stenosis underwent surgical repair that involved extensive reconstruction of the pulmonary arterial tree bilaterally, including the central, lobar and segmental branches. The clinical records of these patients were reviewed for preoperative data, repair techniques and early clinical outcomes, including reinterventions and recent echocardiographic studies.All 7 patients had bilateral peripheral and central pulmonary artery stenosis with systemic to supra-systemic RV pressure and severe RV dysfunction. Median age at the time of operation was 72 months (range=7 to 120 months). All patients had low RV pressure (≤40% systemic) after the procedure with significant improvement in RV function to normal before hospital discharge. The mean right ventricular/left ventricular pressure ratio decreased from 1.27±0.19 to 0.31±0.06 postoperatively (p0.001). There was no mortality. All patients continued regular follow-up at our institution for a mean follow-up period of 17.6±9.1 months. All patients were alive and asymptomatic with normal RV function and no evidence of elevated RV pressure on most recent echocardiography.Complete surgical reconstruction of the pulmonary arterial tree in advanced cases of ATS is a successful intervention with excellent early clinical and hemodynamic outcomes.