预测死亡事件的12个临床特征。 heart_failure_clinical_records_dataset.csv (0)踩踩(0) 所需:13积分 ftbt 2024-12-04 12:46:19 积分:1 PID_Parameters_Auto_Tuning 2024-12-04 12:45:43 积分:1 OneForAll 2024-12-04 12:36:54 积分:1
This paper used a dataset of 299 heart failure patients and applied the Neuro-Fuzzy systems (NFS) to this dataset. This prediction is made by testing each two feature together in the dataset and feeding it to the NFS system to determine its effect on the patients. In this pa...
Heart Failure Prediction (RandomForest - XGBoost).ipynb: The main Jupyter notebook containing the data analysis, model training, and evaluation processes. README.md: This file, providing an overview of the project. Dataset The dataset used in this project contains information related to patients' ...
Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing...
Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using ...
Validation of the prediction model is an essential step in machine learning processes. In this paper, the K-Fold cross-validation method is applied to validating the results of the above-mentioned classification models. K-fold cross validation (CV) In K-Fold CV, the whole dataset is split int...
A majority of the cohort was male (57.4%), and the median age of adults in the dataset was 58 and 63 for males and females, respectively (48, 67 interquartile range for male and 48, 72 female patients), which suggests a relatively young heart failure population and is consistent with ...
Hence, the purpose of this study is to increase the accuracy of previous works on predicting heart failure survival by selecting significant predictive features in order of their ranking and dealing with class imbalance in the classification dataset. In this study, we propose an integrated method ...
Heart failure (HF) admission is a dominant contributor to morbidity and healthcare costs in dilated cardiomyopathy (DCM). Mid-wall striae (MWS) fibrosis by late gadolinium enhancement (LGE) imaging has been associated with elevated arrhythmia risk. However, its capacity to predict HF-specific outco...
The hidden layer structure provides features from the cardiac failure dataset as input. The following parameter values were set: a learning rate of 0.01, a batch size of 256, a dropout rate of 0.2, and 25 epochs. Additionally, the CNN approach-based performance evaluation method is implemented...