{"title":"用 C4.5 决策树算法对布吉丁基 M. Hatta 脑科医院的脑卒中疾病进行分类","authors":"Futiah Salsabila, Zamahsary Martha, Atus Amadi putra, Admi Salma","doi":"10.24036/ujsds/vol2-iss1/135","DOIUrl":null,"url":null,"abstract":"Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"20 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5\",\"authors\":\"Futiah Salsabila, Zamahsary Martha, Atus Amadi putra, Admi Salma\",\"doi\":\"10.24036/ujsds/vol2-iss1/135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.\",\"PeriodicalId\":220933,\"journal\":{\"name\":\"UNP Journal of Statistics and Data Science\",\"volume\":\"20 11\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"UNP Journal of Statistics and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24036/ujsds/vol2-iss1/135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"UNP Journal of Statistics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24036/ujsds/vol2-iss1/135","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
中风是一种有血管病变的健康状况,大脑功能与输送血液到大脑的血管问题有关。影响中风的几个因素包括不健康的饮食习惯、缺乏体育锻炼、吸烟行为、饮酒和肥胖。中风的症状包括头痛、恶心、呕吐、视力模糊和吞咽困难。研究人员的目的是根据 DR 的中风诊断结果,确定影响中风住院率的风险因素。M. Hatta 脑科医院(武吉丁吉市)的中风诊断结果,使用决策树对每个变量进行分类,从而确定影响中风住院率的风险因素。决策树是一种类似分支树的流程图。本研究使用的 C4.5 算法可以处理数值数据和分类数据,可以处理缺失的属性值,并生成易于解释的规则。分析结果表明,作为中风风险因素的属性是心脏。使用 C4.5 算法创建的模型通过计数器融合矩阵进行测试,结果显示准确率为 64.54%,正确分类缺血性中风患者的精确率为 53.34%,正确分类出血性中风患者的召回率为 72.73%。
Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5
Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.