{"title":"利用数据挖掘算法预测糖尿病筛查","authors":"A. Zemedkun","doi":"10.29121/ijoest.v5.i6.2021.253","DOIUrl":null,"url":null,"abstract":"Diabetes is one of the most common non-communicable diseases in the world. Diabetes affects the ability to produce the hormone insulin. Thus, complications may occur if diabetes remains untreated and unidentified. That features a significant contribution to increased morbidity, mortality, and admission rates of patients in both developed and developing countries. When disease is not detected early, it leads to complications. Medical records of the cases were retrospective. Anthropometric and biochemical information was collected. From this data, four ML classification algorithms, including Decision Tree (J48), Naive-Bayes, PART rule induction, and JRIP, were used to prognosticate diabetes. Precision, recall, F-Measure, Receiver Operating Characteristics (ROC) scores, and the confusion matrix were calculated to determine the performance of the various algorithms. The performance was also measured by sensitivity and specificity. They have high classification accuracy and are generally comparable in predicting diabetes and free diabetes patients. Among the selected algorithms tested, the Decision Tree Classifier (J48) algorithm scored the highest accuracy and was the best predictor, with a classification accuracy of 92.74%.","PeriodicalId":331301,"journal":{"name":"International Journal of Engineering Science Technologies","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PREDICTION OF DIABETES SCREENING BY USING DATA MINING ALGORITHMS\",\"authors\":\"A. Zemedkun\",\"doi\":\"10.29121/ijoest.v5.i6.2021.253\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diabetes is one of the most common non-communicable diseases in the world. Diabetes affects the ability to produce the hormone insulin. Thus, complications may occur if diabetes remains untreated and unidentified. That features a significant contribution to increased morbidity, mortality, and admission rates of patients in both developed and developing countries. When disease is not detected early, it leads to complications. Medical records of the cases were retrospective. Anthropometric and biochemical information was collected. From this data, four ML classification algorithms, including Decision Tree (J48), Naive-Bayes, PART rule induction, and JRIP, were used to prognosticate diabetes. Precision, recall, F-Measure, Receiver Operating Characteristics (ROC) scores, and the confusion matrix were calculated to determine the performance of the various algorithms. The performance was also measured by sensitivity and specificity. They have high classification accuracy and are generally comparable in predicting diabetes and free diabetes patients. Among the selected algorithms tested, the Decision Tree Classifier (J48) algorithm scored the highest accuracy and was the best predictor, with a classification accuracy of 92.74%.\",\"PeriodicalId\":331301,\"journal\":{\"name\":\"International Journal of Engineering Science Technologies\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Engineering Science Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29121/ijoest.v5.i6.2021.253\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering Science Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29121/ijoest.v5.i6.2021.253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
糖尿病是世界上最常见的非传染性疾病之一。糖尿病会影响产生胰岛素的能力。因此,如果糖尿病得不到治疗和不明,可能会发生并发症。这是发达国家和发展中国家发病率、死亡率和住院率上升的重要原因。如果不及早发现疾病,就会导致并发症。病例的医疗记录是回顾性的。收集人体测量和生化信息。根据这些数据,四种ML分类算法,包括Decision Tree (J48)、Naive-Bayes、PART规则归纳和JRIP,被用于预测糖尿病。计算精密度、召回率、F-Measure、受试者工作特征(ROC)评分和混淆矩阵,以确定各种算法的性能。通过灵敏度和特异度来衡量其性能。它们具有较高的分类准确性,在预测糖尿病和非糖尿病患者方面具有可比性。在选择的测试算法中,决策树分类器(J48)算法的准确率最高,预测效果最好,分类准确率为92.74%。
PREDICTION OF DIABETES SCREENING BY USING DATA MINING ALGORITHMS
Diabetes is one of the most common non-communicable diseases in the world. Diabetes affects the ability to produce the hormone insulin. Thus, complications may occur if diabetes remains untreated and unidentified. That features a significant contribution to increased morbidity, mortality, and admission rates of patients in both developed and developing countries. When disease is not detected early, it leads to complications. Medical records of the cases were retrospective. Anthropometric and biochemical information was collected. From this data, four ML classification algorithms, including Decision Tree (J48), Naive-Bayes, PART rule induction, and JRIP, were used to prognosticate diabetes. Precision, recall, F-Measure, Receiver Operating Characteristics (ROC) scores, and the confusion matrix were calculated to determine the performance of the various algorithms. The performance was also measured by sensitivity and specificity. They have high classification accuracy and are generally comparable in predicting diabetes and free diabetes patients. Among the selected algorithms tested, the Decision Tree Classifier (J48) algorithm scored the highest accuracy and was the best predictor, with a classification accuracy of 92.74%.