Priyanka Kakade, S. Zope, G. Suragimath, S. Varma, A. Kale, Vaishali Mashalkar
{"title":"非手术牙周治疗对吸烟者和牙周炎患者唾液谷胱甘肽还原酶的影响","authors":"Priyanka Kakade, S. Zope, G. Suragimath, S. Varma, A. Kale, Vaishali Mashalkar","doi":"10.51847/wzghl73bwk","DOIUrl":null,"url":null,"abstract":"Introduction: Data mining is one of the five stages of Knowledge Discovery in Databases (KDD) and its application can be found on a daily basis in the most varied sectors of the economy and study, being widely used in the health area. Health concerns and efforts have been gaining relevance and strength as the entire world has been suffering from the fight against the coronavirus pandemic, especially in underdeveloped and developing countries, such as Brazil and other nations of the American continent.Method: Aiming to contribute to the area, the objective of this study was to apply a data mining task, using the Random Forest algorithm, to classify the population's behavior on the search for health services after the onset of the coronavirus pandemic. The database used was the Premise General Population Covid-19 Health Services Disruption Survey 2020 from the COVID-19 Health Services Disruption Survey 2020 project.Results: Using the Random Forest algorithm, 87.5% accuracy was obtained in the classification of people who whether or not they will use health services in American countries. A recall value of 93% and precision of 85% were reached in the best models.Conclusions: The model developed and the results achieved can be used to assist authorities in American countries on planning public health policies.","PeriodicalId":42752,"journal":{"name":"Annals of Dental Specialty","volume":"2 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effect of Non-Surgical Periodontal Therapy (NSPT) on Salivary Glutathione Reductase (GR) in Smokers And Periodontitis Subjects\",\"authors\":\"Priyanka Kakade, S. Zope, G. Suragimath, S. Varma, A. Kale, Vaishali Mashalkar\",\"doi\":\"10.51847/wzghl73bwk\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: Data mining is one of the five stages of Knowledge Discovery in Databases (KDD) and its application can be found on a daily basis in the most varied sectors of the economy and study, being widely used in the health area. Health concerns and efforts have been gaining relevance and strength as the entire world has been suffering from the fight against the coronavirus pandemic, especially in underdeveloped and developing countries, such as Brazil and other nations of the American continent.Method: Aiming to contribute to the area, the objective of this study was to apply a data mining task, using the Random Forest algorithm, to classify the population's behavior on the search for health services after the onset of the coronavirus pandemic. The database used was the Premise General Population Covid-19 Health Services Disruption Survey 2020 from the COVID-19 Health Services Disruption Survey 2020 project.Results: Using the Random Forest algorithm, 87.5% accuracy was obtained in the classification of people who whether or not they will use health services in American countries. A recall value of 93% and precision of 85% were reached in the best models.Conclusions: The model developed and the results achieved can be used to assist authorities in American countries on planning public health policies.\",\"PeriodicalId\":42752,\"journal\":{\"name\":\"Annals of Dental Specialty\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Dental Specialty\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.51847/wzghl73bwk\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Dental Specialty","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51847/wzghl73bwk","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effect of Non-Surgical Periodontal Therapy (NSPT) on Salivary Glutathione Reductase (GR) in Smokers And Periodontitis Subjects
Introduction: Data mining is one of the five stages of Knowledge Discovery in Databases (KDD) and its application can be found on a daily basis in the most varied sectors of the economy and study, being widely used in the health area. Health concerns and efforts have been gaining relevance and strength as the entire world has been suffering from the fight against the coronavirus pandemic, especially in underdeveloped and developing countries, such as Brazil and other nations of the American continent.Method: Aiming to contribute to the area, the objective of this study was to apply a data mining task, using the Random Forest algorithm, to classify the population's behavior on the search for health services after the onset of the coronavirus pandemic. The database used was the Premise General Population Covid-19 Health Services Disruption Survey 2020 from the COVID-19 Health Services Disruption Survey 2020 project.Results: Using the Random Forest algorithm, 87.5% accuracy was obtained in the classification of people who whether or not they will use health services in American countries. A recall value of 93% and precision of 85% were reached in the best models.Conclusions: The model developed and the results achieved can be used to assist authorities in American countries on planning public health policies.