S. Hussein, Lubab Ahmed Tawfeeq, Sukaina Sh Altyar
{"title":"评价体外冲击波碎石术(ESWL)联合分级治疗肾结石的能力","authors":"S. Hussein, Lubab Ahmed Tawfeeq, Sukaina Sh Altyar","doi":"10.29304/JQCM.2019.11.1.466","DOIUrl":null,"url":null,"abstract":"Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or to using another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classifier (NCC). The samples had been taken from 2850 actual sufferers cases that had been treated at Urology and Nephrology center of Iraq. The results from three cases have been compared to actual treatment results of (ESWL) for trained and non-trained cases and compared the results of three models. The results show that (NCC) approach is the most accurate method in prediction the efficient of uses (ESWL) remedy in treatment the kidney stone.","PeriodicalId":418998,"journal":{"name":"Journal of Al-Qadisiyah for computer science and mathematics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Predicate the Ability of Extracorporeal Shock Wave Lithotripsy (ESWL) to treat the Kidney Stones by used Combined Classifier\",\"authors\":\"S. Hussein, Lubab Ahmed Tawfeeq, Sukaina Sh Altyar\",\"doi\":\"10.29304/JQCM.2019.11.1.466\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or to using another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classifier (NCC). The samples had been taken from 2850 actual sufferers cases that had been treated at Urology and Nephrology center of Iraq. The results from three cases have been compared to actual treatment results of (ESWL) for trained and non-trained cases and compared the results of three models. The results show that (NCC) approach is the most accurate method in prediction the efficient of uses (ESWL) remedy in treatment the kidney stone.\",\"PeriodicalId\":418998,\"journal\":{\"name\":\"Journal of Al-Qadisiyah for computer science and mathematics\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Al-Qadisiyah for computer science and mathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29304/JQCM.2019.11.1.466\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Al-Qadisiyah for computer science and mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29304/JQCM.2019.11.1.466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicate the Ability of Extracorporeal Shock Wave Lithotripsy (ESWL) to treat the Kidney Stones by used Combined Classifier
Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or to using another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classifier (NCC). The samples had been taken from 2850 actual sufferers cases that had been treated at Urology and Nephrology center of Iraq. The results from three cases have been compared to actual treatment results of (ESWL) for trained and non-trained cases and compared the results of three models. The results show that (NCC) approach is the most accurate method in prediction the efficient of uses (ESWL) remedy in treatment the kidney stone.