基于随机森林的癌症数据预测 计算机科学和技术专业VIP专享VIP免费

摘要基于随机森林算法的癌症数据预测—I—摘要摘要近年来，机器学习、深度学习技术在各行各业的应用推动了这些领域的智能化发展。而且，疾病预测技术还给人们带来了深度影响，改变着人们的日常工作、学习和生活，在医学方面，应用计算机相关技术进行疾病的预测已经成为当下研究的热点，医疗数据爆炸增长，已经建立起来了庞大的医疗数据库，有潜在的实用价值。随着以深度学习为代表的计算机相关技术的不断发展与成熟，出现了大数据分析技术与医学健康领域开始紧密结合。本课题主要任务是通过Python开发环境设计基于随机森林算法的癌症数据预测系统，收集病人肿瘤的周长、半径、面积等参数信息，通过构建随机森林模型，对模型进行训练，从而实时预测患者病情。系统实现可以预测患者死亡风险，全面地分析信息之间隐含的内在联系，为癌症患者的病情预防起到关键作用。关键词:随机森林；数据挖掘；癌症预测；机器学习—II—ABSTRACTABSTRACTInrecentyears,Theuseofmachinelearningandin-depthlearningtechnologiesinalllifeenvironmentshaspromotedtheintelligentdevelopmentoftheseareas.Furthermore,diseaseprognosishasasignificantimpactonpeople'sdailylife,workandresearch.Theapplicationofcomputertechnologyinmedicaldiseasepredictionhasbecomeahotspotofcurrentresearch.Theexplosivegrowthofmedicaldatahasestablishedahugemedicaldatabase,whichhaspotentialpracticalvalue.Withthecontinuousdevelopmentandmaturityofbigdataanalysistechnologyrepresentedbydeeplearning,bigdataanalysistechnologyhasbeendeeplycombinedwithmedicalandhealthfield.ThemaintaskofthisprojectistodesignacancerdatapredictionsystembasedonrandomforestalgorithmthroughPythondevelopmentenvironment,collecttheparameterinformationofpatients'tumorperimeter,radius,areaandsoon,andbuildarandomforestmodeltotrainthemodel,soastopredictpatients'conditioninrealtime.Thesystemcanpredictthedeathriskofpatients,comprehensivelyanalyzetheinternalrelationshipbetweeninformation,andplayakeyroleinthepreventionofcancerpatients.Keywords:Randomforest;Datamining;Cancerprediction;machinelearning—III—目录目录摘要........................................................IABSTRACT....................................................................................................................II前言............................................................11绪论........................................................21.1....................................研究背景及意义21.2...............................................研究现状31.2.1大数据挖掘...............................31.3本课题主要工作..............................42相关技术简介.........................................52.1Python语言...........................................52.2数据挖掘.........................................62.2.1数据挖掘方法............................62.2.2数据挖掘流程............................72.3机器学习.........................................82.3.1支持向量机...............................92.3.2随机森林算法............................93系统分析..............................................113.1可行性分析...................................113.1.1技术可行性..............................113.1.2经济可行性..............................113.1.3操作可行性..............................123.2功能需求分析...............................123.3系统性能分析...............................124系统设计..............................................144.1设计目标及原则............................144.2论文研究内容.................................145系统实现............................