Cardiovascular disease, especially heart failure is one of the major health hazard issues of our time and is a leading cause of death worldwide. Advancement in data mining techniques using machine learning (ML) models is paving promising prediction approaches. Data mining is the process of converting massive volumes of raw data created by the healthcare institutions into meaningful information that can aid in making predictions and crucial decisions. Collecting various follow-up data from patients who have had heart failures, analyzing those data, and utilizing several ML models to predict the survival possibility of cardiovascular patients is the key aim of this study. Due to the imbalance of the classes in the dataset, Synthetic Minority Oversampling Technique (SMOTE) has been implemented. Two unsupervised models (K-Means and Fuzzy C-Means clustering) and three supervised classifiers (Random Forest, XGBoost and Decision Tree) have been used in our study. After thorough investigation, our results demonstrate a superior performance of the supervised ML algorithms over unsupervised models. Moreover, we designed and propose a supervised stacked ensemble learning model that can achieve an accuracy, precision, recall and F1 score of 99.98%. Our study shows that only certain attributes collected from the patients are imperative to successfully predict the surviving possibility post heart failure, using supervised ML algorithms.
Interpretable Survival Analysis for Heart Failure Risk Prediction
Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）；Kaiser Permanente Northern California Division of Research（凯撒医疗北加州研究部）；Stanford Medicine（斯坦福医学院）
Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Models
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University（玛希隆大学拉马蒂博迪医院医学院查克里·纳鲁博丁医学研究所）；Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University（玛希隆大学拉马蒂博迪医院医学院临床流行病学与生物统计学系）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；Cardiovascular Unit, Department of Internal Medicine, Police General Hospital（警察总医院内科心血管科）
Deep learning cardiac motion analysis for human survival prediction
National Heart and Lung Institute, Imperial College London, UK（英国帝国理工学院国家心肺研究所）；Department of Computing, Imperial College London, UK（英国帝国理工学院计算机系）；Imperial College Healthcare NHS Trust, London, UK（英国伦敦帝国理工学院医疗NHS信托基金）；Division of Experimental Medicine, Department of Medicine, Imperial College London, UK（英国帝国理工学院医学系实验医学部）；National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore（新加坡国家心脏中心和新加坡杜克-新加坡国立大学医学院）
A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes
Department of Cardiology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University（南京鼓楼医院心内科，南京大学国家药物生物技术重点实验室）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学电子与光学工程学院）；College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学集成电路科学与工程学院）；Department of Cardiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University（南京医科大学南京鼓楼医院临床学院心内科）；Institute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications（南京邮电大学量子信息与技术研究院）
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach
University of New South Wales（新南威尔士大学）；University of Auckland（奥克兰大学）；National Drug and Alcohol Research Centre（国家药物与酒精研究中心）
目标。本研究比较了生存分析模型的深度学习扩展与传统Cox比例风险（CPH）模型在国家卫生管理数据集中推导心血管疾病（CVD）风险预测方程的性能。方法.使用多个管理数据集的个人联系，我们构建了一个年龄在30-74岁之间的所有新西兰居民的队列，这些居民在2012年期间与公共资助的卫生服务进行了互动，并在五年的随访中确定了心血管疾病的住院和死亡。在排除了先前患有CVD或心力衰竭的人之后，开发了性别特异性深度学习和CPH模型，以估计五年内致命或非致命CVD事件的风险。在整个研究人群和特定风险组的模型之间比较了解释的事件发生时间、校准和区分的比例。调查结果。首次CVD事件发生在2，164，872人中的61，927人中。在诊断和手术中，深度学习模型与女性吸烟（2.04，95%CI：1.99-2.10）和男性慢性阻塞性肺疾病伴急性下呼吸道感染（1.56，95%CI：1.50-1.62）相关的“局部”风险比最大。其他已确定的预测因子（例如高血压、胸痛、糖尿病）与当前关于CVD风险预测因子的知识一致。深度学习模型在解释事件发生时间的比例（Royston和Sauerbrei的R平方：女性为0.468 vs. 0.425，男性为0.383 vs. 0.348）、校准和辨别力（所有p<0.0001）方面显著优于CPH模型。解释。生存分析模型的深度学习扩展可以应用于大型卫生管理数据库，以导出比传统CPH模型…
StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection
East West University（东西大学）；Bangladesh Agricultural University（孟加拉国农业大学）；Daffodil International University（达福迪尔国际大学）；University of Dhaka（达卡大学）；University of Barishal（巴里萨尔大学）；Cumilla Victoria Government College（库米拉维多利亚政府学院）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）
MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
Department of Statistics, Texas A&M University（统计学系，德克萨斯A&M大学）；Department of Mathematics, Korea Military Academy（数学系，韩国军事学院）；Department of Statistics, Iowa State University（统计学系，爱荷华州立大学）
Neurochaos Feature Transformation and Classification for Imbalanced Learning
BMS Institute of Technology and Management（BMS理工与管理学院）；National Institute of Advanced Studies（国家高等研究院）；The University of Trans-Disciplinary Health Sciences And Technology（跨学科健康科学与技术大学）
