Cardiovascular Disease Prediction using Recursive Feature Elimination
and Gradient Boosting Classification Techniques
- URL: http://arxiv.org/abs/2106.08889v1
- Date: Fri, 11 Jun 2021 16:17:42 GMT
- Title: Cardiovascular Disease Prediction using Recursive Feature Elimination
and Gradient Boosting Classification Techniques
- Authors: Prasannavenkatesan Theerthagiri, Vidya J
- Abstract summary: This paper proposes a proposed gradient boosting (RFE-GB) algorithm in order to obtain accurate heart disease prediction.
The patients health record with important CVD features has been analyzed for the evaluation of the results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Cardiovascular diseases (CVDs) are one of the most common chronic illnesses
that affect peoples health. Early detection of CVDs can reduce mortality rates
by preventing or reducing the severity of the disease. Machine learning
algorithms are a promising method for identifying risk factors. This paper
proposes a proposed recursive feature elimination-based gradient boosting
(RFE-GB) algorithm in order to obtain accurate heart disease prediction. The
patients health record with important CVD features has been analyzed for the
evaluation of the results. Several other machine learning methods were also
used to build the prediction model, and the results were compared with the
proposed model. The results of this proposed model infer that the combined
recursive feature elimination and gradient boosting algorithm achieves the
highest accuracy (89.7 %). Further, with an area under the curve of 0.84, the
proposed RFE-GB algorithm was found superior and had obtained a substantial
gain over other techniques. Thus, the proposed RFE-GB algorithm will serve as a
prominent model for CVD estimation and treatment.
Related papers
- Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning [5.761426161930679]
This project intends to study a cardiovascular disease risk early warning model based on one-dimensional convolutional neural networks.
The missing values of 13 physiological and symptom indicators such as patient age, blood glucose, cholesterol, and chest pain were filled and Z-score was standardized.
arXiv Detail & Related papers (2024-06-13T07:04:22Z) - Predicting risk of cardiovascular disease using retinal OCT imaging [40.71667870702634]
We investigated the potential of optical coherence tomography as an additional imaging technique to predict future cardiovascular disease (CVD)
We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images.
The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability.
arXiv Detail & Related papers (2024-03-26T14:42:46Z) - Improving Diffusion Models for ECG Imputation with an Augmented Template
Prior [43.6099225257178]
noisy and poor-quality recordings are a major issue for signals collected using mobile health systems.
Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models.
We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
arXiv Detail & Related papers (2023-10-24T11:34:15Z) - An Improved Heart Disease Prediction Using Stacked Ensemble Method [0.9187159782788579]
We constructed an ML-based diagnostic system for heart illness forecasting, using a heart disorder dataset.
Our method can easily differentiate between people who have cardiac disease and those who are normal.
arXiv Detail & Related papers (2023-04-12T17:53:59Z) - Penalized Deep Partially Linear Cox Models with Application to CT Scans
of Lung Cancer Patients [42.09584755334577]
Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies.
The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients.
We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
arXiv Detail & Related papers (2023-03-09T15:38:16Z) - Using Deep Learning-based Features Extracted from CT scans to Predict
Outcomes in COVID-19 Patients [0.4841303207359715]
A novel methodology is proposed by combining multi-modal features, extracted from Computed Tomography (CT) scans and Electronic Health Record (EHR) data.
Deep learning models are leveraged to extract quantitative features from CT scans.
These features combined with those directly read from the EHR database are fed into machine learning models to eventually output the probabilities of patient outcomes.
arXiv Detail & Related papers (2022-05-10T16:22:16Z) - SurvLatent ODE : A Neural ODE based time-to-event model with competing
risks for longitudinal data improves cancer-associated Deep Vein Thrombosis
(DVT) prediction [68.8204255655161]
We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data.
Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function.
SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
arXiv Detail & Related papers (2022-04-20T17:28:08Z) - Machine Learning-Based Classification Algorithms for the Prediction of
Coronary Heart Diseases [0.0]
The study created and tested several machine-learning-based classification models.
The results show that logistic regression produced the highest performance score on the original dataset.
In conclusion, this study suggests that LR on a well-processed and standardized dataset can predict coronary heart disease with greater accuracy than the other algorithms.
arXiv Detail & Related papers (2021-12-02T18:52:56Z) - Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based
Sparse PCA Network [93.22587316229954]
We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E)
We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
arXiv Detail & Related papers (2021-10-27T19:28:36Z) - Ensemble machine learning approach for screening of coronary heart
disease based on echocardiography and risk factors [19.076443235356873]
We develop a machine learning approach that integrates a number of popular classification methods together by model stacking.
We improve the CHD classification accuracy from around 70% to 87.7% on the testing set.
arXiv Detail & Related papers (2021-05-20T11:04:58Z) - Prediction of the onset of cardiovascular diseases from electronic
health records using multi-task gated recurrent units [51.14334174570822]
We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records.
The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
arXiv Detail & Related papers (2020-07-16T17:43:13Z)
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.