Unleashing the Power of Extra-Tree Feature Selection and Random Forest Classifier for Improved Survival Prediction in Heart Failure Patients

• URL: http://arxiv.org/abs/2308.05765v1
• Date: Wed, 9 Aug 2023 11:47:28 GMT
• Title: Unleashing the Power of Extra-Tree Feature Selection and Random Forest Classifier for Improved Survival Prediction in Heart Failure Patients
• Authors: Md. Simul Hasan Talukder, Rejwan Bin Sulaiman, Mouli Bardhan Paul Angon
• Abstract summary: Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes. In this study, we explore the potential of utilizing data pre-processing techniques and the Extra-Tree (ET) feature selection method in conjunction with the Random Forest (RF) classifier to improve survival prediction in heart failure patients. By leveraging the strengths of ET feature selection, we aim to identify the most significant predictors associated with heart failure survival. Using the public UCL Heart failure (HF) survival dataset, we employ the ET feature selection algorithm to identify the most informative features. These features are then used as input for grid search of RF. Finally, the tuned RF Model was trained and evaluated using different matrices. The approach was achieved 98.33% accuracy that is the highest over the exiting work.

Related papers

• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients [0.0]
  For severely affected COVID-19 patients, it is crucial to identify high-risk patients and predict survival and need for intensive care (ICU) This study investigated the performances of nine machine and deep learning algorithms in combination with two widely used feature selection methods. LSTM performed the best in predicting last status and ICU requirement with 90%, 92%, 86% and 95% accuracy, sensitivity, specificity, and AUC respectively.
  arXiv  Detail & Related papers  (2024-03-28T12:11:29Z)
• SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast [4.5445892770974154]
  Survival Rank-N Contrast (SurvRNC) is a loss function as a regularizer to obtain an ordered representation based on the survival times. We demonstrate that using the SurvRNC method for training can achieve higher performance on different deep survival models.
  arXiv  Detail & Related papers  (2024-03-15T18:00:11Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology [40.96261204117952]
  We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ. We show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
  arXiv  Detail & Related papers  (2022-04-09T17:35:37Z)
• Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis [4.067706269490143]
  An efficient survival classification model is presented in a comprehensive manner. A synthetic dataset is generated by imputing the missing values, transforming the data using dummy variable encoding, and compressing the dataset from 59 features to the 11 most correlated features using Chi-squared feature selection. Several supervised ML methods were trained in this regard, like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Gradient Boosting, Ada Boost, and XG Boost.
  arXiv  Detail & Related papers  (2022-01-22T08:01:22Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence [0.0]
  This work presents an explainability analysis and evaluation of a prediction model for heart failure survival. The model employs a data workflow pipeline able to select the best ensemble tree algorithm as well as the best feature selection technique. The paper's main contribution is an explainability-driven approach to select the best prediction model for HF survival based on an accuracy-explainability balance.
  arXiv  Detail & Related papers  (2021-08-20T09:03:26Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• A random shuffle method to expand a narrow dataset and overcome the associated challenges in a clinical study: a heart failure cohort example [50.591267188664666]
  The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate. The proposed random shuffle method was able to enhance the HF dataset cardinality circa 10 times and circa 21 times when followed by a random repeated-measures approach.
  arXiv  Detail & Related papers  (2020-12-12T10:59:38Z)
• Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation [5.050648346154715]
  Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
  arXiv  Detail & Related papers  (2020-01-08T10:04:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.