Pushkar Wankhede, Nandini Sakhare, Yogita K. Dubey, Manu Varghese, Shrutika Gawande, Manoj Patil
{"title":"基于机器学习算法的老年痴呆症分类框架","authors":"Pushkar Wankhede, Nandini Sakhare, Yogita K. Dubey, Manu Varghese, Shrutika Gawande, Manoj Patil","doi":"10.1109/ICETEMS56252.2022.10093641","DOIUrl":null,"url":null,"abstract":"At Least 50 million people in the world are assumed to have Alzheimer Disease (AD). Hence early diagnosis as well as early dose of memantine is required to inhibit further increase of this disease. In this paper a framework is proposed for classification of Alzheimer using five different Machine Learning models. Dataset is taken from Kaggle website after that Data preprocessing takes place. Five different models viz. Logistic Regression, Support Vector Machine, Random Forest, Decision Tree and Gradient Boosting Classifier are compared using various evaluation metrics such as Accuracy, Precision, Recall, Log loss and AUC-ROC.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Framework for Alzheimer Diseases Classification using Machine Learning Algorithms\",\"authors\":\"Pushkar Wankhede, Nandini Sakhare, Yogita K. Dubey, Manu Varghese, Shrutika Gawande, Manoj Patil\",\"doi\":\"10.1109/ICETEMS56252.2022.10093641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At Least 50 million people in the world are assumed to have Alzheimer Disease (AD). Hence early diagnosis as well as early dose of memantine is required to inhibit further increase of this disease. In this paper a framework is proposed for classification of Alzheimer using five different Machine Learning models. Dataset is taken from Kaggle website after that Data preprocessing takes place. Five different models viz. Logistic Regression, Support Vector Machine, Random Forest, Decision Tree and Gradient Boosting Classifier are compared using various evaluation metrics such as Accuracy, Precision, Recall, Log loss and AUC-ROC.\",\"PeriodicalId\":170905,\"journal\":{\"name\":\"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETEMS56252.2022.10093641\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETEMS56252.2022.10093641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Framework for Alzheimer Diseases Classification using Machine Learning Algorithms
At Least 50 million people in the world are assumed to have Alzheimer Disease (AD). Hence early diagnosis as well as early dose of memantine is required to inhibit further increase of this disease. In this paper a framework is proposed for classification of Alzheimer using five different Machine Learning models. Dataset is taken from Kaggle website after that Data preprocessing takes place. Five different models viz. Logistic Regression, Support Vector Machine, Random Forest, Decision Tree and Gradient Boosting Classifier are compared using various evaluation metrics such as Accuracy, Precision, Recall, Log loss and AUC-ROC.