"as per the models are identified to be based on gathering of the clinical data as assumed forthis study. The clinical assessments and the frontal profile of the craniofacial photographicanalysis is being presented which is including the proper management of the analysis withdefining the prior indication to the polysomnography (Hopwood, 2014). This is defining theautomatic image processing systems which processes the facial images as per thedetermination of the likelihood of the subject. This is also the presentation of the practicaland the clinical data, which are used for controlling the subjects, and also the first approachof the discrimination should not be conducted with defining the proper management of thework. This is also including the proper accuracy of the work as per the discrimination of thepatients is being made with showing the logistic classifier and the resulting system is beingmade by defining the resulting system. The second approach, which is considered in thiscase, is defining the neural network, which is designed for carrying out the automatic process,and the frontal profile photographs are included for the classification of the patients areessential to give back them their normal life (Schmitt, 2017). The inclusion of the accuracy isbeing included to be about 62% as per the analysis is being made and also the workings canbe easily illustrated to be showing the results of the appropriate treatment process as adoptedby the structures presented in this case. The previous studies are appropriately related to theanatomical risks factors identified that could be easily mitigated for the welfare of thepatients and also the engagement of the work is showing the proper view of the work withdefining the collapsibility and the obstructions of the sleeping activities. This is simplyillustrating the overall treatment process and the information related to the diseases asincluded in this study. Methods and the machine learning languages The parameters which are explained in this part is defining the satisfaction of the clinicalneeds, and also the parameters are defined as per the selection is being made. The parameter6 | P a g e coefficients are surrounding the classification of the severity of the sleep apnea from the bodyprofile parameters as per the analysis is being made which is supporting the vector machine.The SVM model is presented for viewing the nonlinear regression model as per theappropriate collection of the potential data that is included in this study. SubjectsThe subjects are referred for the polysomnography for the investigation of the obstructivesleep Apnea. The exclusion criteria include the history and evidence from the syndromalcraniofacial abnormalities, facial hair and craniofacial surgery that obscured the faciallandmarks. All ethnicity subjects are included. The standardized process of photographic andclinical assessment is carried on the subjects as polysomnography. The collection of data andanalysis of photographic are carried out on the RL (single investigator) who was blinded tothe outcome of the polysomnography (Valham et al., 2012). Photographic technique The profile and frontal photographs of the neck and head were gathered with the standardizedsetups by using the digital camera with single lens. Before the cartilaginous landmarks,certain bony and photographs are pre-determined on the subjects by marked and palpationwith the white tape. The technique is used for the alignment of the subject, and it is the test ofthe reliability.Craniofacial photogrammetryThe use of the software for image analysis, the photographs are analysed for the landmarkdigitization. The landmarks craniofacial of interest are captures as the pixel coordinated ofthe image that can be transferred to the programmed custom spreadsheet for the calculation of7 | P a g e angular, linear, polyhedral volume and area measurements (Haugen and Musser, 2012). Themeasurement of the pixel is to be converted to the metric dimensions which are based on theconversion cycle of around 53 pixels’/cm. A total number of measurement that is observed is71 and out of which 62 is related to the measurement of craniofacial. The measurementrepresents the relationships and measurements of different craniofacial regions which includethe neck, head, nose, eyes, maxilla, face and mandible.Clinical assessmentThe subject on the demographics, Epworth Sleepiness Scale, comorbidities and OSA weregathered. The anthropometric assessments are included in the waist circumference, bodymass index, and neck circumference. With the standardized technique, the oropharyngealassessment is carried out. It included the estimation of the modified Mallampati class withwide open of the mouth without the lump of the tongue: soft palate, pillars, and tonsils arevisible clearly, uvula, upper pole and pillars are visible. Soft palate’ small part is visible,pharyngeal grade, tonsillar grade, extended tonsil up to 75 percent of the way to midline,complete the tonsils are obstructing, tongue size, uvula size and the presence of the overjet(Henningfeld, 2009). Polysomnography The diagnostic polysomnography is performed on the basis of the previous studies. The sleepstaging is identified using the standardized definitions. The Apnea is described as thecomplete cessation airflow for less than ten seconds with the desaturation of oxygen of atleast three percent which is associated with the arousals.The hypopnea is being described asthe reduction in the thoracoabdominal movement of wall or flow of air of less than 50 percentof the measurement for less than ten seconds with the desaturation of oxygen. The Apnea- 8 | P a g e "