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Heart Disease Prediction Based on an Optimal Feature Selection Method using Autoencoder

Azhar M. A., Princy Ann Thomas
2020 International Journal of Scientific Research in Science and Technology  
In my work, we have used an artificial neural network-based autoencoder for effective feature selection The aim of feature selection is improving prediction performance and providing a better understanding  ...  in the prediction of cardiovascular disease.  ...  Implemented a system for predicting heart disease using Data mining techniques: K Means and Weighted Association rule.  ... 
doi:10.32628/ijsrst20748 fatcat:g63chqbbevartj4egwk34ddujq

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques

Pronab Ghosh, Sami Azam, Mirjam Jonkman, Asif Karim, F.M. Javed Mehedi Shamrat, Eva Ignatious, Shahana Shultana, Abhijit Reddy Beeravolu, Friso De Boer
2021 IEEE Access  
Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease.  ...  For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model.  ...  By using a SVM they obtained the selected feature subset and they used a validation process for MCC calculation. The features were selected based on a higher than average Fisher score.  ... 
doi:10.1109/access.2021.3053759 fatcat:ddnimyh6ezg35ksdaeqwakazk4

A Hybrid Model for Coronary Heart Disease Prediction in Thai Population

Chalinee Partanapat, Chuleerat Jaruskulchai, Chanankorn Jandaeng
2020 Advances in Science, Technology and Engineering Systems  
To investigate the better predictive performance of our study, feature selection methods of both filter and wrapper groups are employed with exploring the hybrid models to identify the most relevant features  ...  The objective of this research is to find the best predictive model for coronary heart disease diagnosis.  ...  In [6] , the authors developed a research on Effective Heart Disease Prediction by employing feature selection as association algorithm.  ... 
doi:10.25046/aj050552 fatcat:5jpnuuphxzbc7omsfv2keyzjpy

Prediction of ARDS Syndrome and CAD Using Multilayer Perceptron

M. Mohanasundari, Prasanth. S
2020 International Journal of Scientific Research in Science and Technology  
Hybrid method for CAD diagnosis, including risk factor identification using correlation based feature subset selection with particle swam optimization search method and K-Means clustering algorithms.  ...  This proposed system is increasing the efficiency and accuracy of predicting the ARDS and CAD diseases.  ...  On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated with risk for CAD heart Disease.  ... 
doi:10.32628/ijsrst207225 fatcat:jbwk27bzm5g5lbhd2vayh4uh6a

Performance Analysis of Various Data Mining Techniques in the Prediction of Heart Disease

Kodali Lohita, Adusumilli Amitha Sree, Doreti Poojitha, T. Renuga Devi, A. Umamakeswari
2015 Indian Journal of Science and Technology  
The second phase is feature selection. The greedy hill climbing best first attribute evaluator is used to identify the subset of attributes based on its individual prediction ability.  ...  Different classification techniques are used to predict heart disease based on the factors that cause these diseases which include family history, age, obesity and some other factors.  ...  Network algorithms in association with feature selection and feature creation algorithms.  ... 
doi:10.17485/ijst/2015/v8i35/87458 fatcat:2z7zfdejfvdrvfng7dmenib4uq

Dynamic Features Selection For Heart Disease Classification

2013 Zenodo  
For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming.  ...  In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading  ...  This combination resulted in a hybrid algorithm that uses RST for pre-processing of data and ANN for classification or prediction.  ... 
doi:10.5281/zenodo.1333514 fatcat:ldn7rcyaj5e3bmsyprjipeit7i

An Automated Diagnostic System for Heart Disease Prediction Based on χ2 Statistical Model and Optimally Configured Deep Neural Network

Liaqat Ali, Atiqur Rahman, Aurangzeb Khan, Mingyi Zhou, Ashir Javeed, Javed Ali Khan
2019 IEEE Access  
reported methods for heart disease prediction.  ...  The findings of the study suggest that the proposed diagnostic system can be used by physicians to accurately predict heart disease.  ...  The results for the subset of features are saved. In second iteration, another subset of features with n = 2 is constructed by selecting the first two features with highest χ 2 score.  ... 
doi:10.1109/access.2019.2904800 fatcat:ard7fxdk6vgzlleqcrtmyohk6q

HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm

M. Dhilsath Fathima, S. Justin Samuel, S. P. Raja
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
The two significant stages of HDDSS are the feature selection stage and the classification modeling stage.  ...  The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset.  ...  As a result, predicting the onset of heart disease at an early stage is essential for controlling risk factors and preventing heart disease.  ... 
doi:10.9781/ijimai.2021.10.003 fatcat:7nmxbxisvnealgguussrr5lwum

A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ2 Statistical Model and Gaussian Naive Bayes

Liaqat Ali, Shafqat Ullah Khan, Noorbakhsh Amiri Golilarz, Imrana Yakubu, Iqbal Qasim, Adeeb Noor, Redhwan Nour
2019 Computational and Mathematical Methods in Medicine  
Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy.  ...  The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects.  ...  Robert Detrano, who gathered HF-related information for the Cleveland heart disease data, used logistical regression to predict HF risk assessment, achieving a classification precision of 77%.  ... 
doi:10.1155/2019/6314328 pmid:31885684 pmcid:PMC6925936 fatcat:m2ylfoupknftvaky2r6l6avoeu

Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis

Afnan M. Alhassan, Wan Mohd Nazmee Wan Zainon
2021 IEEE Access  
The hybrid feature selection eliminates duplicate and irrelevant feature subsets that significantly enhances the performance of classification. D. Jain, and V.  ...  So, the dimension of the data is reduced by the behaviour of PSO and GCSA. The selected features are fed to associative memory classifier for data classification. Y. Khourdifi and M.  ... 
doi:10.1109/access.2021.3088613 fatcat:xrsf434oqbd45gqtpi4x33yl7i

Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Kaushalya Dissanayake, Md Gapar Md Johar, Thunshun W. Liao
2021 Applied Computational Intelligence and Soft Computing  
This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches.  ...  The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with  ...  are useful in increasing the prediction accuracy of weak classifiers and perform well in predicting heart disease risk.  ... 
doi:10.1155/2021/5581806 fatcat:iff3vk7ww5ggblrvhbcuxpk3ia

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

Sushruta Mishra, Pradeep Kumar Mallick, Hrudaya Kumar Tripathy, Akash Kumar Bhoi, Alfonso González-Briones
2020 Applied Sciences  
The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy.  ...  These datasets contain a series of symptoms that are used in disease prediction.  ...  All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10228137 fatcat:ycjcssjz7feubeknseh42gay3m

An improved tree model based on ensemble feature selection for classification

2019 Turkish Journal of Electrical Engineering and Computer Sciences  
The proposed method removes the irrelevant features and selects the optimal features for classification through probability weighting criteria.  ...  This study uses an ensemble-based feature selection using random trees and wrapper method to improve the classification.  ...  In addition to preprocessing, researchers included selecting the best and most relevant features for prediction and classification.  ... 
doi:10.3906/elk-1808-85 fatcat:4ri52o7pwfhlbpzqgri5eqikza

Chronic Kidney Disease (CKD) Prediction Using Supervised Data Mining Techniques

S. Rajarajeswari, T. Tamilarasi
2021 International journal of advanced networking and applications  
Apart from these previous techniques, it was necessary to use a classification method for data segmentation according to their diagnosis and regression method for finding risk factors.  ...  Data mining is an accurate technique helps to predict the disease using various methods includes logistic regression, naive bayes classification, k-nearest neighbours, and support vector machine.  ...  Hybrid Intelligent techniques for the prediction of heart disease were presented by R. Chitra  ... 
doi:10.35444/ijana.2021.12607 fatcat:cemngaw64zeehhib3u7xjyay5e

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Asma Baccouche, Begonya Garcia-Zapirain, Cristian Castillo Olea, Adel Elmaghraby
2020 Information  
performance with accuracy and F1-score between 91% and 96% for the different types of heart disease.  ...  To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection.  ...  ensemble classifier for heartbeat classification [30] , and cardiovascular disease detection using hybrid models [31, 32] .  ... 
doi:10.3390/info11040207 fatcat:sr5sfldyarfctevcgb54ybqtam
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