{"title":"优化的乳腺癌集合预测模型","authors":"Jatin Aditya","doi":"10.1109/ITSS-IoE53029.2021.9615269","DOIUrl":null,"url":null,"abstract":"Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimized Ensemble Prediction Model for Breast Cancer\",\"authors\":\"Jatin Aditya\",\"doi\":\"10.1109/ITSS-IoE53029.2021.9615269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.\",\"PeriodicalId\":230566,\"journal\":{\"name\":\"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSS-IoE53029.2021.9615269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615269","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
与女性有关的乳腺癌被认为是最常见的癌症之一。为了更好的医疗,乳腺癌的早期检测是必不可少的一步。本研究的重点是使用集成机器学习范式进行乳腺癌的自动预测。有监督的机器学习模型使用标记数据进行训练,以感知一个假设,该假设将为特定问题领域提供良好的预测。尽管假设空间包含了非常适合某个特定问题的假设,但要找到一个好的假设可能非常困难。集成学习将多种学习结合起来,形成更好的假设。表达式Ensemble通常用于使用齐次或非齐次基础学习器从各种假设生成预测的方法。在评估这类集成模型时,通常需要额外的计算,而不是评估单一模型的预测。与bagging或boosting不同,我们使用非同质分类器来预测乳腺癌是否是癌性的,即恶性的还是良性的,使用GaussianNB作为scikit learn in python的堆叠分类器中的元分类器,我们使用来自威斯康星州的乳腺癌数据集,由加州大学维护。记录的预测率达到99.41%,优于单一算法的预测率。
Optimized Ensemble Prediction Model for Breast Cancer
Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.