"hypopnea index is estimated as the total number of hypopneas and apneas sleep of each hour.The polysomnography is carried out by the experienced sleep specialists. Statistical and data analysis Obstructive Sleep Apnea are developed using the two differentstatistical methods and approaches, classification, regression tree and logistic regression. Thepresence of the obstructive sleep Apnea is described by the AHI less than ten per hour andwithout Obstructive Sleep Apnea is described by the AHI of less than ten per hour(Marcovitz, 2007). Logistic regression The photographic measurements are considered initially, and the multi-step processes areemployed for the decreasing the number of variables for the analysis. The multi-colinearitydecreased the number of measurement from 133 to 105. There is also decrease in the stepwiseregression of the AHI log transformed for the group of the measurements. The approach hasled to the decreased set of the thirteen variables for the analysis to obtain the ObstructiveSleep Apnea prediction models. The logistic regression ratio of the remaining variables setwas employed to produce the photographic prediction model for the Obstructive SleepApnea. The backward ratio logistic regression is used for developing the clinical andintegrated photographic/clinical prediction models. The total of sixteen clinical variables isconsidered for the models of clinics. The model characteristics, receiver operatingcharacteristic, predictive values, and classification accuracy is being estimated for the model.The threshold probability used for the classification of the Obstructive Sleep Apnea is 0.50.Regression tree and classification The regression tree analysis and classification is the predictive method that utilizes thenonparametric technique for evaluating the data and accounting for the complexrelationships. There is a progressive splitting in the analysis of population in two groups on9 | P a g e the basis of the independent predictive variables. The following variables are selected, thediscriminatory valued and the occurrence of splitting are also produced by the mathematicalalgorithm in order to increase the accuracy of the prediction (Chandra and Sharma, 2013).The ten cross-fold validation process is being applied during the development of CARTmodel to minimize the over fitting of data. The procedure of cross-validation modelingincluded using the 90% proportion of data and validation of other remaining. It is alsorepeating the one-tenth of data and covering all the data. The measurement of the 133photograph and the splits value of the measurement are examined with a CART to constructthe model and separating the subject without and with Obstructive Sleep Apnea. Thedevelopment of classification trees is continuing the cases of splitting in order to get theterminal nodes that are the clusters of the without or with Obstructive Sleep Apnea.Conclusion Linked with the statistical approaches that CARTY analysis given am an alternative approachto test the data, which are based on the progressive binary intense of data, hence avoided theissues of collinearity plus parametric prediction with logistic regression analysis. Moreover,the distinct benefit of the CART is that the CART analysis method is well suited to theformation of the decisions rules. Notably, both the statistical approaches of modelingoutcomes in same expected accuracy plus concordance. Despite the fact that any processes ofmodeling will generate a better fit of the recent data in comparison to the novel data sets.Moreover, the cross-validation methods have given an assessment or measurement of theaccuracy of the within paradigm prediction, which was in the range of 61% to the 76%. Afollowing point of view of the study will be needed to assess the accuracy plus clinical use ofthe entire prediction models. Other process of data analysis like principal component analysiscould give novel ideas into the impact of the craniofacial phenotypes of OSA threats. Inconcluding, the use of a new craniofacial photographic analysis technique, which is10 | P a g e developed is potential and useful for the clinical prediction, and this model is helpful for thedetection of OSA on the basis of craniofacial morphological phenotypes. This approachprobably has other research study plus clinical uses in OSA.ReferencesChandra, S. and Sharma, M. (2013). Research methodology. Oxford: Alpha Science Internat.Haugen, D. and Musser, S. (2012). Health care. Farmington Hills, MI: Greenhaven Press.Henningfeld, D. (2009). Health. Detroit: Greenhaven Press.Hopwood, C. (2014). Multimethod clinical assessment. New York [u.a.]: Guilford Press.Kushida, C. (2007). Obstructive sleep apnea. 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