Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ...Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.展开更多
胃癌具有高度的异质性,准确分类是胃癌诊断、治疗和判定预后的必要条件。传统的组织病理学分型对临床工作的指导意义十分有限。近年来,随着分子生物组学和转录组学研究的发展,胃癌基因组特征、基因表达谱和蛋白质组学等有关信息被全面了...胃癌具有高度的异质性,准确分类是胃癌诊断、治疗和判定预后的必要条件。传统的组织病理学分型对临床工作的指导意义十分有限。近年来,随着分子生物组学和转录组学研究的发展,胃癌基因组特征、基因表达谱和蛋白质组学等有关信息被全面了解,从分子水平重新认识胃癌。现有的胃癌分子分型可归类为新加坡基因分型、癌症和肿瘤基因组谱(Cancer Genome Atlas,TCGA)和亚洲癌症基因组织(Asian Cancer Research Group,ACRG)等分型。然而,分子分型指导精准治疗在临床上的应用还很有限,实现分子分型的临床应用来实现胃癌的精准分子治疗是未来努力的方向,本文将对胃癌的分子分型与精准治疗研究进展作一综述。展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number PNURSP2024R333,Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.
文摘胃癌具有高度的异质性,准确分类是胃癌诊断、治疗和判定预后的必要条件。传统的组织病理学分型对临床工作的指导意义十分有限。近年来,随着分子生物组学和转录组学研究的发展,胃癌基因组特征、基因表达谱和蛋白质组学等有关信息被全面了解,从分子水平重新认识胃癌。现有的胃癌分子分型可归类为新加坡基因分型、癌症和肿瘤基因组谱(Cancer Genome Atlas,TCGA)和亚洲癌症基因组织(Asian Cancer Research Group,ACRG)等分型。然而,分子分型指导精准治疗在临床上的应用还很有限,实现分子分型的临床应用来实现胃癌的精准分子治疗是未来努力的方向,本文将对胃癌的分子分型与精准治疗研究进展作一综述。
