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Ensemble Learning Techniques for Appendicitis Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
K. Sathiyakumarir

K Sathiyakumarir. Ensemble Learning Techniques for Appendicitis Prediction. International Journal of Computer Applications 163(7):8-11, April 2017. BibTeX

	author = {K. Sathiyakumarir},
	title = {Ensemble Learning Techniques for Appendicitis Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {7},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {8-11},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913480},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Appendicitis leftovers the most common cause of decrease belly ache. It retains a common look at all ages. The appendix is an attachment or adjunct like shape. it's miles a wormlike stomach diverticulum extending from the blind cease of the cecum; it varies in period and leads to a blin extremity. Early and correct analysis of appendicitis can lower the contamination and clinic cost by using lowering the put off in prognosis of appendicitis and its related headaches. accurate prognosis of appendicitis is a tough trouble in exercise especially if the patient is just too young or pregnant girls in that radiological check have excessive risk. thus, ultrasonography image evaluation is a good way to reduce the problem. This work affords an attempt to diagnose the appendicitis with the aid of extracting appendix of different levels from the stomach ultrasound picture. Diverse filtering techniques like LEE and FROST strategies are used for noise removal and Marker-controlled Watershed method is used for segmentation of appendicitis. The vicinity of interest (ROI) approach is used to extract the accurate portion of appendix photograph. The feel functions of the segmented ROI are Gary degree Co-prevalence Matrix (GLCM) and form capabilities are extracted for the future cause of classifying appendicitis. In the end, the ensemble gaining knowledge of set of rules is used to classify appendicitis appropriately via the use of an AdaBoost technique. The AdaBoost technique is evaluated the usage of various measures like Resubstitution Loss mistakes, Generalization mistakes, cross-Validation errors, and schooling errors. It gives very low loss errors rate.


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Appendicitis Prediction